# A Comprehensive Study on Face Recognition Biases Beyond Demographics

Philipp Terhörst, Jan Niklas Kolf, Marco Huber, Florian Kirchbuchner, Naser
Damer, Aythami Morales, Julian Fierrez, and Arjan Kuijper P. Terhörst, J.
Kolf, M.Huber, F. Kirchbuchner, N. Damer, and A. Kuijper are with the
Fraunhofer Institute for Computer Graphics Research IGD, Darmstadt, Germany
and with the Technical University of Darmstadt, Darmstadt, Germany. A. Morales
and J. Fierrez are with Biometrics and Data Pattern Analytics Lab - BiDA Lab,
Universidad Autonoma de Madrid, Madrid, Spain. e-mail:
<EMAIL_ADDRESS>
###### Abstract

Face recognition (FR) systems have a growing effect on critical decision-
making processes. Recent works have shown that FR solutions show strong
performance differences based on the user’s demographics. However, to enable a
trustworthy FR technology, it is essential to know the influence of an
extended range of facial attributes on FR beyond demographics. Therefore, in
this work, we analyse FR bias over a wide range of attributes. We investigate
the influence of 47 attributes on the verification performance of two popular
FR models. The experiments were performed on the publicly available MAADFace
attribute database with over 120M high-quality attribute annotations. To
prevent misleading statements about biased performances, we introduced control
group based validity values to decide if unbalanced test data causes the
performance differences. The results demonstrate that also many non-
demographic attributes strongly affect the recognition performance, such as
accessories, hair-styles and colors, face shapes, or facial anomalies. The
observations of this work show the strong need for further advances in making
FR system more robust, explainable, and fair. Moreover, our findings might
help to a better understanding of how FR networks work, to enhance the
robustness of these networks, and to develop more generalized bias-mitigating
face recognition solutions.

###### Index Terms:

Face recognition, Biometrics, Bias, Fairness, Performance differences, Bias
estimation, Soft-Biometrics

##  I Introduction

Large-scale face recognition systems are spreading worldwide [13]. These
systems have a growing effect on the daily life [75] and are increasingly
involved in critical decision-making processes, such as in forensics and law
enforcement. However, recent works [47, 4, 23, 51, 7, 24, 60] showed that
current face recognition solutions possess biases leading to discriminatory
performance differences [56] based on the user’s demographics [64, 71].

From a legal perspective, there are several regulations to prevent such
discrimination, for instance, Article 7 of the Universal Declaration on Human
Rights, Article 14 of the European Convention of Human Rights, or Article 71
of the General Data Protection Regulation (GDPR) [74]. Driven by (a) these
legal efforts to guarantee fairness and (b) the findings that the performance
of current face recognition solutions depends on the user’s demographics,
several approaches were proposed to mitigate demographics-bias in face
recognition technologies. This was achieved through adversarial learning [26,
41, 46, 45], margin-based approaches [76, 34], data augmentation [77, 39, 78],
metric-learning [70], or score normalization [69]. However, the research focus
on demographic-bias does only tackle a minor proportion of all possible
discriminatory effects. Knowing the influence of an extended set of facial
attributes on the face recognition performance will enable the development of
accurate and less discriminatory face recognition systems.

In this work, we aim at investigating the face recognition bias based on a
wide range of attributes beyond demographics. These biases might affect the
fairness [56] but also the security of face recognition systems [31]. Biases
can be identified as learning weakness to be exploited by users with malicious
intentions (i.e. vulnerability attacks [50]). To be precise, we analyse the
differential outcome as defined by Howard et al. [33] of two popular face
recognition models (FaceNet [55] and ArcFace [17]) with regard to 47
attributes. The experiments are conducted on the recently published and
publicly available MAAD-Face111https://github.com/pterhoer/MAAD-Face
annotation database [66] based on VGGFace2 [8]. It consists of over 120M high-
quality attribute annotations for 3.3M face images. For the experiments,
several decision thresholds are taken into account to cover a wide range of
applications. To prevent misinterpretations of the results origin from testing
data with (a) unbalanced label distributions or (b) attribute correlations, we
(a) introduce control groups to derive a validity value for the recognition
performance in the presence of a specific attribute and (b) analyse the
pairwise correlations of the attribute annotations. While (a) allows us to
quantify results that arise from unbalanced testing data and prevent falsified
statements about the attribute-related bias, (b) emphasize if an attribute
bias might originate from a different (correlated) attribute. Besides a
detailed analysis, we present a visual summary that states the performance
difference between samples with and without a specific attribute over the
validity of the results. This aims to present the results in a compact and
simply understandable manner.

The results support the findings of previous works stating that face
recognition systems have to deal with demographic-biases [57]. We
differentiate between explicit demographic attributes such as gender, age or
ethnicity and non-explicit demographic attributes such as accessories,
hairstyles and -colors, face shapes, or facial anomalies. However, we have to
consider that some of the non-explicit demographic attributes might be
affected by implicit demographic covariates. For example, the hairstyle is
highly affected by gender or ethnicity. The results demonstrate that also many
of the non-demographic attributes strongly affect the recognition performance.
Investigating two face recognition models that differ only in the loss
function used during training, we showed the effect of the underlying training
principles on recognition. While the triplet-loss based FaceNet model showed
attribute-related differential outcomes that are relatively constant on
several decision thresholds, the angular margin based ArcFace model showed
differential outcomes that are often dependent on the used decision threshold.
Many performance differences affected by attributes could be explained through
the attribute’s relation to the visibility of a face, the temporal
variability, and the degree of abnormality. However, our experiment also
reveals many unconventional results that future work have to address. Our
findings strongly motivate further advances in making recognition systems more
robust against covariates [43, 27], explainable [5, 48], and fair [57, 49]. We
hope that these findings help to develop robust and bias-mitigating face
recognition solutions and help also to move forward bias-aware and bias-
mitigating technology in other AI application areas.

##  II Related Work

The phenomena of bias in face biometrics were found in several disciplines
such as presentation attack detection [19, 32], the estimation of facial
characteristics [67, 15], and the assessment of face image quality [68]. In
general, one of the main reasons for bias might be the induction of non-
equally distributed classes in training data [39, 34, 58] that leads to
differences in the recognition performance and thus, might have an unfair
impact, e.g. on specific subgroups of the population. Howard et al. [33]
introduced the terms differential performance and outcome for classifying
biometric performance differentials that separately considers the effect of
false positive and false negative outcomes. They show that the often-cited
evidence regarding biometric equitability has focused primarily on false-
negatives.

Previous works on bias in face recognition [18] mainly focused on the
influence of demographics. However, Terhörst et al. [65] demonstrated recently
that more (non-demographic) characteristics are stored in face templates that
might have an impact on the face recognition performance. In the following, we
will shortly discuss related works on estimating and mitigating bias in face
biometrics. For a more complete overview, we refer to [18].

###  II-A Estimating Bias in Face Recognition

In recent years, several works have been published that demonstrated the
influence of demographics on commercial and open-sources face recognition
algorithms. Studies [53, 44, 16, 63] analysing the impact of age demonstrated
a lower biometric performance on faces of children. Studies [73, 3, 2, 58]
analysing the effect of gender on face recognition showed that the recognition
performance of females is weaker than the performance on male faces.
Experiments without unbalanced data distributions and with an unbalanced
towards female faces resulted in similar results [3]. In [2], experiments with
a PCA-decomposition showed that females faces are intrinsically more similar
than male ones. Research analysing the impact of the user’s ethnicity showed
faces of ethnicities which were under-represented in the training process
perform significantly weaker. The same was found for darker-skinned cohorts in
general [40].

More recent studies [25, 38, 62, 12, 35, 9, 33, 54, 28, 6] focused on jointly
investigating the effects of user demographics on face recognition. These
studies showed that the effects lead to an exponential face recognition error
increase when facing the same biased race, gender, and age factors [33].
Particular attention deserves the Face Recognition Vendor Test (FRVT) [28], a
large-scale benchmark of commercial algorithms analysing the face recognition
performance with regards to demographics. They consistently elevated false
positives for female subjects and subjects at the outer ends of the age
spectrum. An overview of bias estimation in face recognition is shown in Table
I.

TABLE I: Overview of recent works analysing bias in face recognition. Identities and images refers to the used testing data. In contrast to previous works that analyses some specific demographic attributes, our work investigates a large range of demographic and non-demographic attributes. Work | Identities | Images | Attributes (number classes)
---|---|---|---
Ricanek et al. [53] | 0.7k | 8.0k | Age (2)
Deb et al. [16] | 0.9k | 3.7k | Age (cont.)
Srinivas et al. [63] | 1.7k | 9.2k | Age (2)
Michalski et al. [44] | - | 4.7M | Age (cont.)
Albiero et al. [2] | 26.9k | 151.6k | Gender (2)
Albiero et al. [3] | 15.9k | 101.3k | Gender (2)
Vera-Rodriguez et al. [73] | 0.5k | 169.4k | Gender (2)
Cavazos et al. [9] | 0.4k | 1.1k | Ethnicity (2)
Krishnapriya et al [40] | 22.7k | 3.3M | Gender (2), Ethnicity (2)
Serna et al. [57] | 55k | 1.4M | Gender (2), Ethnicity (4)
Acien et al. [1] | 1.7k | 13k | Gender (2), Ethnicity (3)
Hupont et al. [35] | 0.6k | 10.8k | Gender, Ethnicity (3)
Robinson et al. [54] | 0.8k | 2.0k | Gender (2), Ethnicity (4)
Srinivas et al. [62] | 0.7k | 8.0k | Age (cont.), Gender (2)
Klare et al. [38] | 52.3k | 102.9k | Age (3), Gender (2),
|  |  | Ethnicity (3)
Howard et al. [33] | 1.1k | 2.7k | Age (cont.), Gender (2),
|  |  | Ethnicity (2)
Grother et al. [28] | 8.0M | 18.0M | Age (5), Gender (2),
|  |  | Ethnicity (4)
Georgopoulos et al [25] | 1.0k | 41.0k | Age (5), Gender (2),
|  |  | Kinship (5)
Balakrishnan et al. [6] | 1.3k | 1.3k | Gender (2), Hair (cont.)
|  |  | Ethnicity (cont.)
Cook et al. [12] | 1.1k | 2.7k | Age (cont.), Gender (2),
|  |  | Ethnicity (4), Eyewear (2)
Lu et al. [43] | 5.4k | 162.5k | Demographics (3),
|  |  | Non-demographics (4)
This work | 9.1k | 3.3M |  Demographics (8),
|  |  | Non-demographics (40)
|  |  |

###  II-B Mitigating Bias in Face Recognition

The findings summarized in Section II-A motivated research towards mitigating
demographic-bias in face recognition approaches. An early approach was
presented by Zhang and Zhou [80] who formulated the face verification problem
as a multiclass cost-sensitive learning task and demonstrated that this
approach can reduce different kinds of faulty decisions of the system. In
2017, range loss [79] was proposed to learn robust face representations that
can deal with long-tailed training data. It is designed to reduce overall
intrapersonal variations while enlarging interpersonal differences
simultaneously. Recent works aimed at mitigating demographic-bias in face
recognition through adversarial learning [26, 41, 46, 45], margin-based
approaches [76, 34], data augmentation [77, 39, 78], metric-learning [70], or
score normalization [69].

###  II-C How This Work Contributes to the State of the Art

So far, the majority of research in estimating and mitigating bias in face
recognition focused on demographic factors such as age, gender, and race.
However, to achieve a generally accurate and fair face recognition model, it
is necessary to know all potential origins of differential outcome. Therefore,
this work aims at closing this knowledge gap by analysing the differential
outcome on a much wider attribute range than previous works (see Table I).
More precisely, this work investigates the influence of 47 attributes on the
face recognition performance of two popular face embeddings. The 47 attributes
represent a step forward in the literature in comparison with previous
analyses focused on no more than seven attributes [43].

##  III Experiments on Measuring Differential Outcome

###  III-A Database and Considered Attributes

To get reliable statements on the effect of different attributes on face
recognition, we need a database that (a) provides a high number of face images
with (b) many attribute annotations of (c) high quality. For the experiments,
we choose the publicly available MAAD-Face222https://github.com/pterhoer/MAAD-
Face annotation database [66] based on the images of VGGFace2 [8] since this
database fulfils our experimental requirements. MAAD-Face provides over 120M
high-quality attribute annotations of 3.3M face images of over 9k individuals.
It provides annotations for 47 distinct attributes of various kinds such as
demographics, skin types, hair-styles and -colors, face geometry, annotations
for the periocular, mouth, and nose area, as well as annotations for
accessories. An exact list of the MAAD-Face annotation attributes can be seen
in Table II and III. These attribute annotations proofed to have a higher
quality than comparable face annotation databases [66].

###  III-B Face Recognition Models

For the experiments, we use two popular face recognition models, FaceNet [55]
and ArcFace [17]. To create a face embedding for a given face image, the image
has to be aligned, scaled, and cropped. Then, the preprocessed image is passed
to a face recognition model to extract the embeddings. For FaceNet, the
preprocessing is done as described in [37]. To extract the embeddings, a
pretrained model333https://github.com/davidsandberg/facenet was used. For
ArcFace, the image preprocessing was done as described in [29] and a
pretrained model444https://github.com/deepinsight/insightface is used, which
is provided by the authors of ArcFace. Both models use a ResNet-100
architecture and were trained on the MS1M database [30]. The identity
verification is done by comparing two embeddings with the widely-used cosine-
similarity.

###  III-C Evaluation Metrics

The face verification performance is reported in terms of (a) false non-match
rates (FNMR) at a fixed false match rate (FMR) and (b) equal error rates
(EER). The EER equals the FMR at the threshold where FMR = FNMR and is well
known as a single-value indicator of the verification performance. The used
error rates are specified for biometric verification evaluation in the
international standard [36]. In the experiments, the face verification
performance is reported on three operating points to cover a wide range of
potential applications. This includes EER, as well as, the FNMR at $10^{-3}$
and $10^{-4}$ FMR as recommended by the best practice guidelines for automated
border control of the European Boarder Guard Agency Frontex [22]. For each
operating point and attribute, the verification performance is computed on all
samples with positive and all samples with negative annotations. This will
allow to compare the performance differences of face embeddings regarding
binary attributes, such as bald vs non-bald faces.

###  III-D Control Groups

During the experiments, the number of testing samples with positive and
negative labels might be significantly different. To prevent misleading
conclusions from such unbalanced annotation distributions, we introduce
positive and negative control groups for each attribute. For each attribute,
six positive and six negative control groups are created by randomly selecting
samples from the database. This control group creation is done such that the
synthetic control groups have the same number of samples as their positive and
negative counterparts.

Comparing the verification performance of the positive and negative control
groups allow us to state the validity of the (real) attribute-based
verification performance. If the performances of the negative and positive
control groups is very similar, the (real) attribute recognition performance
is treated as valid. In this case, the unbalanced testing data distribution
shows no effect on the performance. If the relative performance of the control
groups differ strongly, the recognition performance might be significantly
affected from the unbalanced distribution of the positively and negatively
annotated samples. In this case, the (real) attribute recognition performance
might be affected as well. Consequently, statements about the influence of
this attribute on the recognition performance are of low validity. In the
experiments, the validity $val$ of an attribute $a$

$\displaystyle
val(a)=1-\dfrac{err_{control}^{(+)}(a)}{err_{control}^{(-)}(a)},$ (1)

is defined over the relative performance differences between the control
groups. The terms $err_{control}^{(+)}(a)$ and $err_{control}^{(-)}(a)$
represent the recognition errors of the positive $(+)$ and the negative $(-)$
control groups of attribute $a$. For the experiments, we consider attributes
with a validity of $<0.9$ as not valid. However, we will also present the
performance differences with the corresponding validity values so that the
operators are able to choose more suitable validity threshold for their
applications.

###  III-E Investigations

To analyse the influence of different attributes on the recognition
performance of two face recognition models, the investigations are divided
into several parts.

  1. 1.

To emphasize if an attribute bias might originate from correlated attribute
annotations, we analyse the correlation between the attribute annotations.

  2. 2.

For each attribute, the recognition performance of its positively- and
negatively-labelled attribute groups are compared to investigate the influence
of this attribute on the recognition performance. The results are discussed in
the context of the corresponding validity values to avoid misinterpretations
occurring from unbalanced testing data.

  3. 3.

A visual summary is provided to relate the impact of the attributes on the
face recognition systems to the validity of the results. This aims at
providing a compact and easily-understandable overview of the findings of this
work.

  4. 4.

We provide possible explanations causing the differential outcome and discuss
these differences between both face recognition systems.

  5. 5.

Lastly, we use the observations to derive future research directions for face
recognition systems.

##  IV Results

###  IV-A Investigating the Correlation of Facial Attributes

To understand the quality of the used labels and potential biases in the
attribute space, Figure 1 shows a selection of specific attribute-label
correlations. The attributes are chosen to show the 15 most positive and
negative pairwise correlations. It can be seen that Wearing Lipstick, Wearing
Earrings, Heavy Makeup, Young, and Attractive correlates highly positively
with Arched Eyebrows, Wavy Hair, and Rosy Cheeks. In contrast, these
attributes correlates negatively with Square Face, Male, and Bags Under Eyes.
These correlations have to be considered when comparing the differential
outcome for the different attributes. However, the correlation matrix also
approves the quality of some labels that semantically excludes each other. For
instance, 5 o Clock Shadow negatively correlates with No Beard and Eyeglasses
negatively correlates with No Eyewear.

Figure 1: Compressed annotation correlations of the used MAAD-Face database.
The attributes are chosen such that the 15 most positive and negative pairwise
are visible. Green indicate positive correlations, while red indicate a
negative correlation. The correlation is based on the Pearson coefficient.
When interpreting the results from Section IV-B highly-correlated attributes
should be considered to prevent misinterpretations.

###  IV-B The Impact of Facial Attributes on Recognition

The main contribution of this work is an analysis of the effect of 47 distinct
attributes on two popular face recognition models. This aims at investigating
model biases. For each attribute, the face verification performance is
calculated on positively-labelled samples, as well as on negatively-labelled
samples. This is done on three operating points as explained in Section III-C.
The relative performance between the positive and negative groups allows to
investigate potential biases of the face recognition model towards the
analysed attribute. To determine if differential outcome results from
unbalanced data distributions we introduced control groups as explained in
Section III-D.

In Tables II, III, IV, and V the performance of the positive and negative
class is shown for each attribute. The performance of the annotated data is
referred as Real while the performance of the control groups is referred as
Control. The relative performance (Rel. Perf.) shows the relative performance
difference between the positive and negative attribute classes. If the
relative performances between the control classes are below 10% ($val\geq
0.9)$, the result is considered as valid (green highlighting). Otherwise, the
result is considered as not valid indicated by a grey highlighting. Positive
values for the relative performance of an attribute represents a positive
effect of the attribute on the face recognition performance. Negative values
indicate a negative influence of the attribute on the recognition performance.
In the following, we present the results of our study on bias on FaceNet and
ArcFace embeddings.

####  IV-B1 Biases in FaceNet Embeddings

The results of our attribute-related study on differential outcome of the
FaceNet model are shown in Table II and III.

Previous works focused on differential outcome affected by the user’s
demographics. The results on FaceNet confirms the observations of these works.
Demographics strongly affect the recognition performance. One of the strongest
impacts on FaceNet is observed for ethnicities. For the investigated FaceNet
model, Asian and Black faces lead to significantly lower recognition rates
than White faces. Also Young ones perform significantly weaker than Middle-
aged faces. Concerning gender, we observe that Male face perform better then
Female ones. These finding are intensively discussed in previous works [2, 3].
However, the experimental results show that there are many more aspects that
strongly affect the recognition performance.

One factor leading to differential outcome is the user’s hair. While Bald
faces and Receding Hairlines lead to a improved recognition performance, Wavy
Hair styles or Bangs are observed to degrade the performance. This can be
explained by the visibility of the face. In general, Wavy Hair and Bangs are
more likely to cover parts of the face while Bald faces or faces with Receding
Hairlines do not occlude part of the faces.

A contradictory observation can be made for facial hair. Faces with No-Beard
perform worse than faces with a beard, such as a 5 o Clock Shadow. A reason
for this can be that people might keep their beards over a long period of time
and thus, the training and testing data might be biased such that the
recognition networks consider the beards for recognition.

Also the color of the hair has an impact on the FaceNet embeddings. While
Blond Hair shows a strongly degraded face recognition performance, Gray Hair
leads to the strongest performances.

The results indicate that the shape of a face only have a minor impact on the
face recognition performance. For Oval Faces, no significant differences to
non-oval faces could be observed. Although, a positive effect on the
recognition performance is shown for Square Faces, in Section IV-A a strong
correlation between Square Face and Male was shown. This might explain the
behaviour.

Faces with High Cheekbones, Double Chins, and Chubby faces also perform better
for FaceNet features than the inverted counterparts. Probably because these
properties provide additional information that can be used for recognition. In
contrast to this, an Obstructed Forehead strongly degrades the recognition
performance while a Fully Visible Forehead provides additional (uncovered)
information that supports the recognition process.

Anomalous properties in the periocular area, such as Bags Under Eyes, Bushy
Eyebrows, or Arched Eyebrows, lead to better recognition rates compared to
face images without these attributes. The same goes for Big Nose and Pointy
Nose.

The reason that Smiling and a Mouth Closed lead to a stronger recognition
performances than non-neutral expressions might be explainable through the
used face databases that mainly contain of face with these expression. In
[14], the opposite effect was already shown by demonstrating that crazy faces
result in low comparison scores. However, this considered extreme expressions
aim at avoiding identification.

Interestingly, accessories have a strong influence on the recognition
performance of FaceNet. Wearing Hat, Wearing Earrings, or _Eyeglasses_ degrade
the face recognition performance significantly and might be explained by the
fact that these accessories cover parts of the face.

####  IV-B2 Biases in ArcFace Embeddings

The results of our attribute-related study on differential outcome of the
ArcFace model are shown in Table IV and V.

Similar to FaceNet, the results on ArcFace confirms the observed demographic
performance differences shown by previous works. For the investigated ArcFace
model, Young faces perform weaker than Middle-aged or Senior faces.
Interestingly, the intensively discussed gender bias is strongly dependent on
the used decision threshold. Especially for lower FMRs the differential
outcome between Male and Female increases. Concerning the ethnic-bias on the
ArcFace model, we are not able to confirm the observations from previous
works. For White faces, the performance is significantly higher than for non-
white faces. For Asian and Black faces, a strong degradation in the
recognition performance can be observed. However, we have to consider this
results as not valid, since we can observe strong performance differences on
the control groups. This indicates that these results are strongly influenced
by the unbalanced testing data.

Similar to FaceNet, the user’s hair shows to have a significant impact on the
face recognition performance. While, Receding Hairlines, Wavy Hair and
Sideburns supports the recognition process, faces with Bangs show a strong
degradation. Again, the performance differences on ArcFace show to be
threshold-dependent. For Wavy Hair, the positive effect on face recognition
vanishes for lower FMRs, and for Bangs, the negative effect increases
drastically for lower FMRs.

Also the color of the user’s hair have an impact on the recognition
performance. Gray Hair performs significantly above average, while Black Hair
performs significantly below average. Blond Hair and Brown Hair lead to
differential outcome depending on the decision threshold. For high FMRs, Blond
Hair improves the recognition performance, while for lower FMRs, the
recognition performance changes to below-average. For faces with Brown Hair,
the positive effect on recognition vanishes for lower FMRs.

The effect of wearing a beard on the performance of ArcFace is similar to
FaceNet. Having No Beard decreases the recognition performance and having a
beard, such as a 5 o Clock Shadow, enhances the recognition. These effects are
clearer for lower FMRs.

On contrast to FaceNet, the face shape effects the recognition performance of
ArcFace. Both, Oval Faces and Square Faces have positive effect on the
recognition performance, which is dependent on the utilized decision
threshold. Round Faces show a strongly degraded recognition. However, a large
fraction of this performance differences can be explained by the unbalanced
data distribution and thus, we have to neglect the results for Round Faces.

Similar to FaceNet, High Cheekbones, Double Chin, Chubby, and a Fully Visible
Forehead lead to improved face recognition performances. While a Fully Visible
Forehead refers to no partial occlusions of the face that might negatively
infer, the other attributes provide anomalous characteristics that might help
for recognition.

Surprisingly, faces with Brown Eyes perform drastically weaker than faces with
non-brown eyes. For Bags Under Eyes, Bushy Eyebrows, and Arched Eyebrows, an
improved face recognition performance can be observed. These attributes can be
treated as anomalies and thus, can support the recognition process. The same
goes for Big Nose and Pointy Nose.

Similar to FaceNet, accessories have a strong impact on the differential
outcome of ArcFace. While having Heavy Makeup, such as Wearing Lipstick,
improves the recognition, faces with Eyeglasses or Wearing Hat lead to strong
degradations in the face recognition performance. A reason for this might be
that people using Heavy Makeup frequently. Consequently, a person in the
training data might either have no or only Heavy Makeup images. On the other
side, people tend to change their Eyeglasses or (Wearing) Hats more
frequently. Moreover, these attributes might lead to partial occlusions of the
face leading to less identity-information available and thus, to a degraded
face recognition performance.

TABLE II: FaceNet - Part 1/2. Face recognition performance based on several attributes. Category | Attribute | Class | EER | FNMR@FMR=$10^{-3}$ | FNMR@FMR=$10^{-4}$
---|---|---|---|---|---
|  |  | Real | Control | Real | Control | Real | Control
Demographics | Male | Positive | 6.64% | 6.49% | 33.28% | 32.51% | 53.64% | 52.44%
|  | Negative | 7.87% | 6.46% | 42.47% | 32.40% | 62.55% | 52.32%
PaleGreen2 White |  White | Rel. Perf. | 15.56% | -0.42% | 21.63% | -0.35% | 14.24% | -0.21%
| Young | Positive | 6.91% | 6.46% | 39.39% | 32.39% | 60.37% | 52.30%
|  | Negative | 5.73% | 6.47% | 28.93% | 32.37% | 48.97% | 52.18%
PaleGreen2 White |  White | Rel. Perf. | -20.58% | 0.12% | -36.19% | -0.08% | -23.27% | -0.22%
| Middle_Aged | Positive | 5.41% | 6.33% | 28.77% | 31.70% | 48.75% | 51.25%
|  | Negative | 6.96% | 6.48% | 36.77% | 32.52% | 57.70% | 52.45%
PaleGreen2 White |  White | Rel. Perf. | 22.29% | 2.33% | 21.74% | 2.52% | 15.52% | 2.28%
| Senior | Positive | 6.01% | 6.23% | 30.26% | 31.19% | 50.52% | 50.52%
|  | Negative | 6.69% | 6.49% | 34.19% | 32.54% | 54.58% | 52.53%
PaleGreen2 White |  White | Rel. Perf. | 10.16% | 3.93% | 11.50% | 4.16% | 7.44% | 3.82%
| Asian | Positive | 11.16% | 5.91% | 69.46% | 29.52% | 88.48% | 48.20%
|  | Negative | 6.33% | 6.49% | 31.91% | 32.55% | 51.27% | 52.54%
PaleGreen2 White |  White | Rel. Perf. | -76.30% | 8.88% | -117.66% | 9.33% | -72.58% | 8.28%
| White | Positive | 5.97% | 6.48% | 31.28% | 32.51% | 50.15% | 52.50%
|  | Negative | 7.51% | 6.44% | 46.82% | 32.16% | 69.44% | 51.94%
PaleGreen2 White |  White | Rel. Perf. | 20.54% | -0.56% | 33.18% | -1.11% | 27.79% | -1.07%
| Black | Positive | 8.85% | 6.02% | 52.50% | 30.20% | 73.61% | 49.32%
|  | Negative | 6.61% | 6.49% | 33.47% | 32.54% | 53.34% | 52.52%
PaleGreen2 White |  White | Rel. Perf. | -33.98% | 7.14% | -56.89% | 7.19% | -37.99% | 6.09%
Skin | Rosy_Cheeks | Positive | 1.29% | 5.46% | 3.76% | 26.05% | 9.46% | 42.72%
|  | Negative | 7.36% | 6.48% | 37.03% | 32.51% | 57.65% | 52.47%
Snow2 White |  White | Rel. Perf. | 82.42% | 15.80% | 89.86% | 19.87% | 83.59% | 18.59%
| Shiny_Skin | Positive | 6.08% | 6.41% | 36.43% | 32.05% | 57.46% | 51.83%
|  | Negative | 7.90% | 6.47% | 41.33% | 32.37% | 62.43% | 52.29%
PaleGreen2 White |  White | Rel. Perf. | 23.06% | 0.85% | 11.86% | 0.99% | 7.97% | 0.88%
Hair | Bald | Positive | 5.10% | 6.13% | 30.52% | 30.69% | 52.37% | 49.93%
|  | Negative | 6.70% | 6.49% | 34.13% | 32.54% | 54.47% | 52.52%
PaleGreen2 White |  White | Rel. Perf. | 23.89% | 5.55% | 10.59% | 5.70% | 3.85% | 4.94%
| Wavy_Hair | Positive | 7.55% | 6.46% | 40.97% | 32.29% | 60.69% | 52.10%
|  | Negative | 6.82% | 6.48% | 34.23% | 32.50% | 54.84% | 52.48%
PaleGreen2 White |  White | Rel. Perf. | -10.68% | 0.34% | -19.69% | 0.65% | -10.65% | 0.73%
| Receding_Hairline | Positive | 4.93% | 6.43% | 26.02% | 32.06% | 44.95% | 51.75%
|  | Negative | 7.35% | 6.47% | 39.92% | 32.46% | 61.12% | 52.43%
PaleGreen2 White |  White | Rel. Perf. | 32.90% | 0.74% | 34.82% | 1.23% | 26.46% | 1.29%
| Bangs | Positive | 6.82% | 6.34% | 45.53% | 31.63% | 69.28% | 51.14%
|  | Negative | 6.43% | 6.49% | 32.02% | 32.54% | 51.86% | 52.52%
PaleGreen2 White |  White | Rel. Perf. | -5.99% | 2.25% | -42.17% | 2.79% | -33.59% | 2.62%
| Sideburns | Positive | 6.68% | 6.46% | 34.33% | 32.34% | 54.08% | 52.23%
|  | Negative | 6.75% | 6.49% | 35.47% | 32.52% | 55.96% | 52.46%
PaleGreen2 White |  White | Rel. Perf. | 1.04% | 0.43% | 3.24% | 0.53% | 3.35% | 0.44%
| Black_Hair | Positive | 7.13% | 6.42% | 42.35% | 32.06% | 65.73% | 51.73%
|  | Negative | 6.20% | 6.48% | 32.46% | 32.49% | 52.06% | 52.47%
PaleGreen2 White |  White | Rel. Perf. | -15.04% | 1.02% | -30.47% | 1.34% | -26.26% | 1.40%
| Blond_Hair | Positive | 9.63% | 6.34% | 52.00% | 31.66% | 71.71% | 51.18%
|  | Negative | 6.45% | 6.48% | 32.63% | 32.52% | 52.96% | 52.48%
PaleGreen2 White |  White | Rel. Perf. | -49.35% | 2.17% | -59.37% | 2.66% | -35.41% | 2.48%
| Brown_Hair | Positive | 7.40% | 6.45% | 39.73% | 32.26% | 59.12% | 52.06%
|  | Negative | 6.19% | 6.47% | 35.13% | 32.41% | 57.09% | 52.30%
PaleGreen2 White |  White | Rel. Perf. | -19.52% | 0.26% | -13.08% | 0.49% | -3.55% | 0.48%
| Gray_Hair | Positive | 5.32% | 6.29% | 26.00% | 31.50% | 44.11% | 50.99%
|  | Negative | 6.72% | 6.49% | 34.60% | 32.54% | 55.25% | 52.52%
PaleGreen2 White |  White | Rel. Perf. | 20.83% | 3.05% | 24.83% | 3.20% | 20.17% | 2.90%
Beard | No_Beard | Positive | 7.20% | 6.48% | 37.97% | 32.49% | 58.83% | 52.44%
|  | Negative | 6.13% | 6.40% | 31.07% | 31.94% | 51.01% | 51.60%
PaleGreen2 White |  White | Rel. Perf. | -17.53% | -1.38% | -22.20% | -1.74% | -15.33% | -1.62%
| Mustache | Positive | 6.45% | 4.93% | 50.77% | 22.55% | 73.71% | 36.74%
|  | Negative | 6.90% | 6.48% | 35.54% | 32.52% | 56.12% | 52.49%
Snow2 White |  White | Rel. Perf. | 6.41% | 23.94% | -42.88% | 30.68% | -31.34% | 30.01%
| 5_o_Clock_Shadow | Positive | 6.16% | 6.38% | 30.98% | 31.87% | 50.01% | 51.53%
|  | Negative | 7.49% | 6.48% | 39.91% | 32.46% | 60.86% | 52.39%
PaleGreen2 White |  White | Rel. Perf. | 17.78% | 1.54% | 22.37% | 1.83% | 17.83% | 1.64%
| Goatee | Positive | 2.59% | 4.69% | 18.78% | 20.11% | 38.17% | 32.98%
|  | Negative | 6.92% | 6.49% | 35.49% | 32.54% | 56.11% | 52.55%
Snow2 White |  White | Rel. Perf. | 62.59% | 27.63% | 47.09% | 38.19% | 31.97% | 37.24%
|  |  |  |  |  |  |  |
TABLE III: FaceNet - Part 2/2. Face recognition performance based on several attributes. Category | Attribute | Class | EER | FNMR@FMR=$10^{-3}$ | FNMR@FMR=$10^{-4}$
---|---|---|---|---|---
|  |  | Real | Control | Real | Control | Real | Control
Face Geometry | Oval_Face | Positive | 8.14% | 6.40% | 45.16% | 31.97% | 64.96% | 51.64%
|  | Negative | 8.26% | 6.46% | 45.11% | 32.30% | 67.44% | 52.08%
PaleGreen2 White |  White | Rel. Perf. | 1.45% | 1.01% | -0.11% | 1.00% | 3.68% | 0.84%
| Square_Face | Positive | 6.32% | 6.48% | 31.37% | 32.49% | 51.25% | 52.44%
|  | Negative | 7.81% | 6.47% | 41.51% | 32.43% | 61.90% | 52.38%
PaleGreen2 White |  White | Rel. Perf. | 19.13% | -0.12% | 24.42% | -0.16% | 17.20% | -0.12%
| Round_Face | Positive | 16.53% | 4.52% | 88.11% | 19.03% | 93.33% | 31.40%
|  | Negative | 5.31% | 6.49% | 27.06% | 32.52% | 45.05% | 52.46%
Snow2 White |  White | Rel. Perf. | -211.14% | 30.27% | -225.65% | 41.49% | -107.17% | 40.14%
| Double_Chin | Positive | 5.45% | 6.44% | 26.28% | 32.15% | 44.43% | 51.85%
|  | Negative | 7.09% | 6.48% | 38.20% | 32.51% | 59.43% | 52.46%
PaleGreen2 White |  White | Rel. Perf. | 23.05% | 0.71% | 31.20% | 1.10% | 25.24% | 1.18%
| High_Cheekbones | Positive | 5.99% | 6.46% | 33.69% | 32.27% | 53.73% | 52.10%
|  | Negative | 8.10% | 6.47% | 41.66% | 32.41% | 62.29% | 52.32%
PaleGreen2 White |  White | Rel. Perf. | 26.11% | 0.20% | 19.13% | 0.43% | 13.73% | 0.43%
| Chubby | Positive | 5.11% | 6.38% | 26.98% | 31.81% | 47.76% | 51.48%
|  | Negative | 6.85% | 6.49% | 36.65% | 32.54% | 57.76% | 52.49%
PaleGreen2 White |  White | Rel. Perf. | 25.35% | 1.62% | 26.38% | 2.23% | 17.31% | 1.92%
| Obstructed_Forehead | Positive | 8.85% | 6.11% | 60.01% | 30.67% | 80.51% | 49.92%
|  | Negative | 6.02% | 6.49% | 31.14% | 32.52% | 50.70% | 52.50%
PaleGreen2 White |  White | Rel. Perf. | -46.87% | 5.75% | -92.69% | 5.69% | -58.79% | 4.91%
| Fully_Visible_Forehead | Positive | 5.47% | 6.48% | 28.25% | 32.46% | 47.36% | 52.35%
|  | Negative | 7.82% | 6.45% | 44.34% | 32.28% | 66.70% | 52.09%
PaleGreen2 White |  White | Rel. Perf. | 30.01% | -0.43% | 36.29% | -0.55% | 28.99% | -0.49%
Periocular | Brown_Eyes | Positive | 7.54% | 6.48% | 42.04% | 32.44% | 63.89% | 52.36%
|  | Negative | 6.12% | 6.36% | 33.59% | 31.83% | 52.03% | 51.50%
PaleGreen2 White |  White | Rel. Perf. | -23.28% | -1.81% | -25.15% | -1.94% | -22.78% | -1.67%
| Bags_Under_Eyes | Positive | 5.90% | 6.45% | 31.51% | 32.31% | 52.50% | 52.16%
|  | Negative | 8.03% | 6.47% | 42.47% | 32.42% | 62.85% | 52.31%
PaleGreen2 White |  White | Rel. Perf. | 26.47% | 0.36% | 25.79% | 0.37% | 16.48% | 0.29%
| Bushy_Eyebrows | Positive | 5.66% | 6.47% | 29.86% | 32.36% | 49.67% | 52.29%
|  | Negative | 7.26% | 6.48% | 37.79% | 32.51% | 58.28% | 52.45%
PaleGreen2 White |  White | Rel. Perf. | 22.03% | 0.23% | 21.00% | 0.44% | 14.77% | 0.31%
| Arched_Eyebrows | Positive | 5.99% | 6.46% | 33.71% | 32.28% | 52.99% | 52.06%
|  | Negative | 7.59% | 6.48% | 38.64% | 32.47% | 59.96% | 52.40%
PaleGreen2 White |  White | Rel. Perf. | 21.10% | 0.37% | 12.75% | 0.58% | 11.62% | 0.64%
Mouth | Mouth_Closed | Positive | 5.25% | 5.99% | 27.84% | 29.97% | 46.77% | 48.87%
|  | Negative | 7.05% | 6.41% | 46.08% | 32.00% | 68.38% | 51.71%
PaleGreen2 White |  White | Rel. Perf. | 25.49% | 6.53% | 39.60% | 6.34% | 31.60% | 5.50%
| Smiling | Positive | 6.08% | 6.44% | 34.06% | 32.17% | 53.51% | 51.91%
|  | Negative | 8.67% | 6.46% | 47.88% | 32.36% | 70.12% | 52.23%
PaleGreen2 White |  White | Rel. Perf. | 29.86% | 0.28% | 28.87% | 0.58% | 23.68% | 0.61%
| Big_Lips | Positive | 6.79% | 6.45% | 39.95% | 32.33% | 61.39% | 52.19%
|  | Negative | 6.97% | 6.47% | 34.09% | 32.44% | 53.99% | 52.36%
PaleGreen2 White |  White | Rel. Perf. | 2.58% | 0.32% | -17.20% | 0.31% | -13.72% | 0.31%
Nose | Big_Nose | Positive | 6.28% | 6.42% | 36.68% | 32.04% | 59.22% | 51.82%
|  | Negative | 8.40% | 6.48% | 46.15% | 32.43% | 67.05% | 52.32%
PaleGreen2 White |  White | Rel. Perf. | 25.23% | 0.90% | 20.52% | 1.20% | 11.67% | 0.94%
| Pointy_Nose | Positive | 6.04% | 6.48% | 32.67% | 32.48% | 51.66% | 52.44%
|  | Negative | 7.80% | 6.46% | 43.90% | 32.32% | 65.97% | 52.22%
PaleGreen2 White |  White | Rel. Perf. | 22.56% | -0.33% | 25.57% | -0.49% | 21.69% | -0.42%
Accessories | Heavy_Makeup | Positive | 6.25% | 6.46% | 35.96% | 32.31% | 55.91% | 52.17%
|  | Negative | 7.08% | 6.49% | 34.76% | 32.52% | 54.97% | 52.48%
PaleGreen2 White |  White | Rel. Perf. | 11.70% | 0.46% | -3.44% | 0.62% | -1.71% | 0.59%
| Wearing_Hat | Positive | 9.01% | 6.24% | 55.58% | 31.23% | 77.17% | 50.65%
|  | Negative | 6.05% | 6.49% | 30.40% | 32.54% | 49.86% | 52.55%
PaleGreen2 White |  White | Rel. Perf. | -48.74% | 3.78% | -82.84% | 4.03% | -54.77% | 3.60%
| Wearing_Earrings | Positive | 7.54% | 6.46% | 41.92% | 32.35% | 61.83% | 52.25%
|  | Negative | 6.78% | 6.48% | 33.84% | 32.49% | 54.34% | 52.45%
PaleGreen2 White |  White | Rel. Perf. | -11.15% | 0.25% | -23.89% | 0.43% | -13.79% | 0.37%
| Wearing_Necktie | Positive | 3.99% | 6.36% | 19.72% | 31.65% | 37.81% | 51.23%
|  | Negative | 7.53% | 6.48% | 41.03% | 32.52% | 62.50% | 52.47%
PaleGreen2 White |  White | Rel. Perf. | 47.05% | 1.88% | 51.93% | 2.65% | 39.51% | 2.37%
| Wearing_Lipstick | Positive | 6.74% | 6.46% | 38.36% | 32.37% | 58.49% | 52.29%
|  | Negative | 7.01% | 6.49% | 34.54% | 32.51% | 54.78% | 52.49%
PaleGreen2 White |  White | Rel. Perf. | 3.91% | 0.39% | -11.05% | 0.44% | -6.78% | 0.39%
| No_Eyewear | Positive | 5.77% | 6.48% | 29.39% | 32.53% | 48.75% | 52.51%
|  | Negative | 6.64% | 6.11% | 37.21% | 30.64% | 63.01% | 49.90%
PaleGreen2 White |  White | Rel. Perf. | 13.10% | -6.09% | 21.03% | -6.16% | 22.63% | -5.24%
| Eyeglasses | Positive | 7.79% | 6.33% | 43.15% | 31.57% | 65.99% | 51.15%
|  | Negative | 5.70% | 6.49% | 29.16% | 32.54% | 48.78% | 52.52%
PaleGreen2 White |  White | Rel. Perf. | -36.65% | 2.51% | -47.99% | 3.00% | -35.27% | 2.61%
Other | Attractive | Positive | 6.27% | 6.45% | 36.28% | 32.31% | 56.11% | 52.10%
|  | Negative | 7.05% | 6.49% | 34.77% | 32.51% | 54.96% | 52.50%
PaleGreen2 White |  White | Rel. Perf. | 11.16% | 0.51% | -4.35% | 0.61% | -2.09% | 0.77%
|  |  |  |  |  |  |  |
TABLE IV: ArcFace - Part 1/2. Face recognition performance based on several attributes. Category | Attribute | Class | EER | FNMR@FMR=$10^{-3}$ | FNMR@FMR=$10^{-4}$
---|---|---|---|---|---
|  |  | Real | Control | Real | Control | Real | Control
Demographics | Male | Positive | 3.98% | 3.98% | 7.07% | 7.22% | 9.71% | 10.17%
|  | Negative | 3.82% | 3.96% | 7.99% | 7.20% | 12.33% | 10.13%
PaleGreen2 White |  White | Rel. Perf. | -4.35% | -0.38% | 11.54% | -0.38% | 21.24% | -0.37%
| Young | Positive | 3.74% | 3.97% | 7.30% | 7.20% | 11.08% | 10.14%
|  | Negative | 3.70% | 3.95% | 6.32% | 7.17% | 8.52% | 10.11%
PaleGreen2 White |  White | Rel. Perf. | -0.86% | -0.46% | -15.42% | -0.46% | -30.08% | -0.28%
| Middle_Aged | Positive | 3.01% | 3.81% | 5.05% | 6.93% | 6.93% | 9.80%
|  | Negative | 4.07% | 3.98% | 7.79% | 7.22% | 11.36% | 10.17%
PaleGreen2 White |  White | Rel. Perf. | 26.14% | 4.05% | 35.20% | 4.04% | 39.04% | 3.56%
| Senior | Positive | 2.95% | 3.62% | 4.52% | 6.58% | 6.15% | 9.38%
|  | Negative | 4.02% | 3.98% | 7.47% | 7.24% | 10.62% | 10.18%
PaleGreen2 White |  White | Rel. Perf. | 26.60% | 9.02% | 39.44% | 9.09% | 42.13% | 7.87%
| Asian | Positive | 7.99% | 3.29% | 16.68% | 6.01% | 22.59% | 8.69%
|  | Negative | 3.73% | 3.98% | 6.75% | 7.23% | 9.61% | 10.18%
Snow2 White |  White | Rel. Perf. | -114.49% | 17.22% | -147.13% | 16.84% | -134.94% | 14.60%
| White | Positive | 3.27% | 3.98% | 5.84% | 7.23% | 8.55% | 10.18%
|  | Negative | 5.80% | 3.91% | 11.69% | 7.10% | 16.03% | 10.01%
PaleGreen2 White |  White | Rel. Perf. | 43.50% | -1.66% | 50.08% | -1.87% | 46.66% | -1.74%
| Black | Positive | 5.72% | 3.40% | 10.90% | 6.21% | 15.02% | 8.95%
|  | Negative | 3.85% | 3.98% | 7.06% | 7.23% | 10.11% | 10.18%
Snow2 White |  White | Rel. Perf. | -48.63% | 14.53% | -54.43% | 14.16% | -48.64% | 12.08%
Skin | Rosy_Cheeks | Positive | 0.98% | 2.91% | 1.17% | 5.12% | 1.31% | 7.47%
|  | Negative | 4.39% | 3.98% | 8.33% | 7.23% | 11.77% | 10.16%
Snow2 White |  White | Rel. Perf. | 77.61% | 26.88% | 85.99% | 29.13% | 88.86% | 26.51%
| Shiny_Skin | Positive | 3.50% | 3.93% | 6.33% | 7.13% | 9.27% | 10.04%
|  | Negative | 4.17% | 3.96% | 8.13% | 7.18% | 11.89% | 10.11%
PaleGreen2 White |  White | Rel. Perf. | 16.14% | 0.61% | 22.13% | 0.72% | 22.04% | 0.73%
Hair | Bald | Positive | 2.79% | 3.50% | 4.48% | 6.38% | 6.07% | 9.14%
|  | Negative | 4.01% | 3.98% | 7.43% | 7.23% | 10.62% | 10.18%
Snow2 White |  White | Rel. Perf. | 30.40% | 12.13% | 39.77% | 11.78% | 42.83% | 10.21%
| Wavy_Hair | Positive | 3.03% | 3.95% | 6.34% | 7.17% | 10.28% | 10.09%
|  | Negative | 4.35% | 3.97% | 7.92% | 7.23% | 10.82% | 10.17%
PaleGreen2 White |  White | Rel. Perf. | 30.46% | 0.73% | 19.95% | 0.84% | 4.96% | 0.80%
| Receding_Hairline | Positive | 3.03% | 3.92% | 4.68% | 7.12% | 6.13% | 10.04%
|  | Negative | 4.10% | 3.97% | 8.20% | 7.22% | 12.25% | 10.15%
PaleGreen2 White |  White | Rel. Perf. | 26.21% | 1.18% | 42.90% | 1.28% | 49.98% | 1.17%
| Bangs | Positive | 4.03% | 3.80% | 8.79% | 6.91% | 13.94% | 9.78%
|  | Negative | 3.83% | 3.98% | 6.77% | 7.23% | 9.42% | 10.17%
PaleGreen2 White |  White | Rel. Perf. | -5.11% | 4.43% | -29.80% | 4.44% | -47.96% | 3.89%
| Sideburns | Positive | 3.72% | 3.97% | 6.51% | 7.21% | 9.10% | 10.13%
|  | Negative | 3.98% | 3.97% | 7.62% | 7.22% | 11.10% | 10.16%
PaleGreen2 White |  White | Rel. Perf. | 6.58% | 0.08% | 14.56% | 0.12% | 18.07% | 0.30%
| Black_Hair | Positive | 5.12% | 3.92% | 9.85% | 7.11% | 13.47% | 10.01%
|  | Negative | 3.48% | 3.97% | 6.36% | 7.21% | 9.28% | 10.15%
PaleGreen2 White |  White | Rel. Perf. | -47.25% | 1.28% | -54.86% | 1.46% | -45.17% | 1.38%
| Blond_Hair | Positive | 3.09% | 3.81% | 7.38% | 6.92% | 12.43% | 9.76%
|  | Negative | 4.09% | 3.98% | 7.34% | 7.23% | 10.16% | 10.18%
PaleGreen2 White |  White | Rel. Perf. | 24.53% | 4.22% | -0.57% | 4.25% | -22.38% | 4.07%
| Brown_Hair | Positive | 3.24% | 3.96% | 6.46% | 7.18% | 10.26% | 10.10%
|  | Negative | 4.12% | 3.97% | 7.59% | 7.20% | 10.59% | 10.14%
PaleGreen2 White |  White | Rel. Perf. | 21.36% | 0.35% | 14.93% | 0.26% | 3.11% | 0.36%
| Gray_Hair | Positive | 2.68% | 3.76% | 4.01% | 6.82% | 5.40% | 9.67%
|  | Negative | 4.07% | 3.98% | 7.57% | 7.23% | 10.77% | 10.17%
PaleGreen2 White |  White | Rel. Perf. | 34.09% | 5.58% | 47.01% | 5.70% | 49.87% | 4.97%
Beard | No_Beard | Positive | 4.13% | 3.98% | 8.10% | 7.23% | 11.93% | 10.18%
|  | Negative | 3.31% | 3.89% | 5.61% | 7.06% | 7.90% | 9.95%
PaleGreen2 White |  White | Rel. Perf. | -25.05% | -2.23% | -44.32% | -2.49% | -50.91% | -2.28%
| Mustache | Positive | 4.89% | 2.63% | 9.62% | 4.61% | 13.54% | 6.68%
|  | Negative | 4.06% | 3.98% | 7.62% | 7.23% | 10.97% | 10.17%
Snow2 White |  White | Rel. Perf. | -20.46% | 33.85% | -26.25% | 36.24% | -23.41% | 34.31%
| 5_o_Clock_Shadow | Positive | 2.96% | 3.90% | 4.94% | 7.06% | 7.08% | 9.96%
|  | Negative | 4.24% | 3.96% | 8.55% | 7.20% | 12.68% | 10.14%
PaleGreen2 White |  White | Rel. Perf. | 30.18% | 1.74% | 42.23% | 1.97% | 44.16% | 1.75%
| Goatee | Positive | 1.18% | 2.46% | 1.68% | 4.00% | 2.67% | 5.89%
|  | Negative | 4.08% | 3.98% | 7.67% | 7.23% | 11.03% | 10.18%
Snow2 White |  White | Rel. Perf. | 71.16% | 38.16% | 78.13% | 44.74% | 75.83% | 42.12%
|  |  |  |  |  |  |  |
TABLE V: ArcFace - Part 2/2. Face recognition performance based on several attributes. Category | Attribute | Class | EER | FNMR@FMR=$10^{-3}$ | FNMR@FMR=$10^{-4}$
---|---|---|---|---|---
|  |  | Real | Control | Real | Control | Real | Control
Face Geometry | Oval_Face | Positive | 2.73% | 3.90% | 5.69% | 7.07% | 9.65% | 9.97%
|  | Negative | 5.40% | 3.96% | 11.10% | 7.19% | 15.61% | 10.12%
PaleGreen2 White |  White | Rel. Perf. | 49.55% | 1.59% | 48.72% | 1.67% | 38.22% | 1.39%
| Square_Face | Positive | 3.73% | 3.97% | 6.37% | 7.22% | 8.68% | 10.16%
|  | Negative | 4.13% | 3.97% | 8.61% | 7.21% | 13.02% | 10.14%
PaleGreen2 White |  White | Rel. Perf. | 9.65% | 0.03% | 25.96% | -0.10% | 33.37% | -0.15%
| Round_Face | Positive | 7.04% | 2.30% | 22.68% | 3.89% | 35.87% | 5.53%
|  | Negative | 3.17% | 3.98% | 5.30% | 7.22% | 7.43% | 10.16%
Snow2 White |  White | Rel. Perf. | -122.46% | 42.18% | -328.09% | 46.22% | -383.05% | 45.63%
| Double_Chin | Positive | 3.34% | 3.93% | 5.32% | 7.12% | 7.00% | 10.04%
|  | Negative | 4.08% | 3.98% | 7.84% | 7.23% | 11.50% | 10.17%
PaleGreen2 White |  White | Rel. Perf. | 18.22% | 1.23% | 32.24% | 1.43% | 39.15% | 1.29%
| High_Cheekbones | Positive | 3.34% | 3.95% | 5.96% | 7.17% | 8.63% | 10.10%
|  | Negative | 4.28% | 3.97% | 8.60% | 7.20% | 12.70% | 10.13%
PaleGreen2 White |  White | Rel. Perf. | 21.87% | 0.48% | 30.76% | 0.42% | 32.08% | 0.34%
| Chubby | Positive | 3.70% | 3.87% | 6.11% | 7.01% | 7.86% | 9.90%
|  | Negative | 3.90% | 3.97% | 7.37% | 7.22% | 10.79% | 10.17%
PaleGreen2 White |  White | Rel. Perf. | 5.18% | 2.62% | 17.14% | 2.85% | 27.15% | 2.58%
| Obstructed_Forehead | Positive | 5.48% | 3.51% | 13.03% | 6.39% | 20.40% | 9.17%
|  | Negative | 3.52% | 3.97% | 6.10% | 7.21% | 8.56% | 10.15%
Snow2 White |  White | Rel. Perf. | -55.61% | 11.62% | -113.74% | 11.37% | -138.28% | 9.66%
| Fully_Visible_Forehead | Positive | 3.30% | 3.97% | 5.47% | 7.21% | 7.49% | 10.15%
|  | Negative | 4.64% | 3.95% | 9.98% | 7.16% | 15.06% | 10.06%
PaleGreen2 White |  White | Rel. Perf. | 28.85% | -0.46% | 45.15% | -0.70% | 50.30% | -0.86%
Periocular | Brown_Eyes | Positive | 4.69% | 3.97% | 9.13% | 7.21% | 12.88% | 10.14%
|  | Negative | 2.63% | 3.85% | 5.36% | 6.98% | 8.73% | 9.85%
PaleGreen2 White |  White | Rel. Perf. | -78.48% | -2.96% | -70.17% | -3.28% | -47.51% | -2.92%
| Bags_Under_Eyes | Positive | 3.78% | 3.96% | 6.37% | 7.19% | 8.48% | 10.11%
|  | Negative | 3.87% | 3.96% | 8.17% | 7.19% | 12.63% | 10.12%
PaleGreen2 White |  White | Rel. Perf. | 2.20% | 0.13% | 22.05% | 0.01% | 32.84% | 0.10%
| Bushy_Eyebrows | Positive | 3.51% | 3.96% | 6.05% | 7.19% | 8.28% | 10.11%
|  | Negative | 4.00% | 3.97% | 7.81% | 7.22% | 11.58% | 10.17%
PaleGreen2 White |  White | Rel. Perf. | 12.35% | 0.20% | 22.54% | 0.54% | 28.46% | 0.60%
| Arched_Eyebrows | Positive | 3.21% | 3.94% | 6.08% | 7.15% | 9.29% | 10.05%
|  | Negative | 4.42% | 3.97% | 8.38% | 7.23% | 11.79% | 10.17%
PaleGreen2 White |  White | Rel. Perf. | 27.52% | 0.81% | 27.46% | 1.05% | 21.20% | 1.14%
Mouth | Mouth_Closed | Positive | 3.06% | 3.37% | 5.40% | 6.13% | 7.70% | 8.85%
|  | Negative | 3.88% | 3.90% | 7.79% | 7.08% | 11.93% | 9.99%
Snow2 White |  White | Rel. Perf. | 21.21% | 13.69% | 30.62% | 13.38% | 35.48% | 11.39%
| Smiling | Positive | 3.35% | 3.94% | 5.93% | 7.14% | 8.48% | 10.05%
|  | Negative | 4.62% | 3.96% | 9.65% | 7.19% | 14.57% | 10.12%
PaleGreen2 White |  White | Rel. Perf. | 27.32% | 0.56% | 38.59% | 0.71% | 41.81% | 0.65%
| Big_Lips | Positive | 4.15% | 3.94% | 8.12% | 7.17% | 11.91% | 10.10%
|  | Negative | 3.88% | 3.97% | 7.00% | 7.22% | 9.84% | 10.16%
PaleGreen2 White |  White | Rel. Perf. | -6.93% | 0.78% | -16.08% | 0.75% | -21.01% | 0.63%
Nose | Big_Nose | Positive | 4.39% | 3.90% | 7.89% | 7.07% | 10.48% | 9.95%
|  | Negative | 3.90% | 3.95% | 8.62% | 7.18% | 13.58% | 10.10%
PaleGreen2 White |  White | Rel. Perf. | -12.67% | 1.49% | 8.52% | 1.51% | 22.80% | 1.46%
| Pointy_Nose | Positive | 3.15% | 3.97% | 5.84% | 7.22% | 8.86% | 10.16%
|  | Negative | 5.28% | 3.96% | 10.46% | 7.18% | 14.62% | 10.11%
PaleGreen2 White |  White | Rel. Perf. | 40.44% | -0.43% | 44.19% | -0.63% | 39.44% | -0.49%
Accessories | Heavy_Makeup | Positive | 3.08% | 3.96% | 5.79% | 7.20% | 9.00% | 10.13%
|  | Negative | 4.32% | 3.97% | 8.02% | 7.22% | 11.14% | 10.16%
PaleGreen2 White |  White | Rel. Perf. | 28.75% | 0.18% | 27.75% | 0.24% | 19.27% | 0.32%
| Wearing_Hat | Positive | 5.51% | 3.66% | 12.28% | 6.62% | 18.45% | 9.44%
|  | Negative | 3.71% | 3.98% | 6.53% | 7.23% | 9.14% | 10.18%
PaleGreen2 White |  White | Rel. Perf. | -48.79% | 8.09% | -88.01% | 8.45% | -101.94% | 7.29%
| Wearing_Earrings | Positive | 3.25% | 3.95% | 6.64% | 7.17% | 10.59% | 10.10%
|  | Negative | 4.08% | 3.98% | 7.33% | 7.23% | 10.10% | 10.17%
PaleGreen2 White |  White | Rel. Perf. | 20.23% | 0.83% | 9.44% | 0.80% | -4.92% | 0.78%
| Wearing_Necktie | Positive | 2.72% | 3.82% | 3.84% | 6.92% | 4.72% | 9.79%
|  | Negative | 4.25% | 3.97% | 8.52% | 7.22% | 12.68% | 10.16%
PaleGreen2 White |  White | Rel. Perf. | 35.95% | 4.00% | 54.94% | 4.24% | 62.77% | 3.73%
| Wearing_Lipstick | Positive | 3.28% | 3.96% | 6.38% | 7.19% | 9.93% | 10.11%
|  | Negative | 4.27% | 3.98% | 7.85% | 7.23% | 10.83% | 10.18%
PaleGreen2 White |  White | Rel. Perf. | 23.21% | 0.40% | 18.74% | 0.57% | 8.25% | 0.65%
| No_Eyewear | Positive | 3.64% | 3.98% | 6.39% | 7.23% | 8.92% | 10.18%
|  | Negative | 3.86% | 3.50% | 6.62% | 6.35% | 8.99% | 9.12%
Snow2 White |  White | Rel. Perf. | 5.75% | -13.57% | 3.42% | -13.84% | 0.82% | -11.60%
| Eyeglasses | Positive | 4.60% | 3.79% | 9.13% | 6.88% | 13.03% | 9.75%
|  | Negative | 3.68% | 3.98% | 6.45% | 7.23% | 8.99% | 10.18%
PaleGreen2 White |  White | Rel. Perf. | -25.08% | 4.88% | -41.53% | 4.89% | -44.86% | 4.24%
Other | Attractive | Positive | 2.95% | 3.96% | 5.49% | 7.19% | 8.60% | 10.10%
|  | Negative | 4.30% | 3.97% | 8.02% | 7.22% | 11.14% | 10.17%
PaleGreen2 White |  White | Rel. Perf. | 31.44% | 0.20% | 31.57% | 0.47% | 22.82% | 0.65%
|  |  |  |  |  |  |  |

(a) FaceNet

(b) ArcFace

Figure 2: Visual summary on the differential outcome affected by each
attribute. Figure 2a visualizes the results for FaceNet, while Figure 2b
visualizes the results for ArcFace. The relative performance is based on the
recognition performance on the positively-labelled data versus the performance
of negatively-labelled data. The validity is based on the performance
differences of the control groups. Validity values below 0.9 (more than 10%
performance differences between the control groups) are considered as not
valid (grey area) and are not shown in this figure. The red areas indicate an
attribute-related bias that leads to a degraded face recognition performance
for faces with the specific attribute. Green areas indicate that faces
possessing a specific attribute enhances the recognition performance. It can
be observed that the majority of the investigated attributes strongly affects
the recognition performance.

###  IV-C Performance Analysis

To provide an overview of the findings, Figure 2 shows the relative
performance differences on FaceNet and ArcFace features based on the
investigated attributes. The shown relative performance is based on the FMR at
$10^{-3}$ FNMR as recommended by the European Boarder Guard Agency Frontex
[22]. The validity describes the performance difference between the positive
and negative attribute-related control groups as shown in Equation 1. An
attribute performance with a validity of less then $90\%$ is considered as not
valid (grey area) since the unbalanced data annotations might affect the
reported performance. The red area indicates that the recognition performance
of the positive attribute class is significantly weaker than the performance
of the negative class. In contrast, the green area indicates a significant
improvement of recognition performance of the positive attribute class over
the negative class. If an attribute has only a minor effect on the recognition
performance, the relative performance is close to 0% (yellow area).

####  IV-C1 FaceNet vs ArcFace

The main difference between FaceNet and ArcFace are the underlying training-
principles. FaceNet uses triplet-loss learning [55] that aims solely at
minimizing the intra-class variations while maximizing the inter-class
variations. In contrast, ArcFace introduces an angular large-margin principle
[17] that additionally aims at enhancing the robustness of recognition model.
The utilized training principle together with the used network structure and
the training data determines the recognition behaviour. This includes the
effect of differential outcome appearing when certain attributes of the face
are present. Since the used FaceNet and ArcFace models share the same network
structure and training data, the observed differential outcome might arise
from the training principles.

####  IV-C2 The Effect of Attributes on Recognition

It turns out that the majority of the investigated attributes strongly affect
the recognition performance of both, FaceNet and ArcFace. For FaceNet, many
faces that are perceived as Attractive or make use of Heavy Makeup do not show
to alter the recognition performance unlike previously reported in [52]. The
same goes for Oval Faces and faces with Sideburns. For ArcFace, Blond Hair,
Big Nose, Big Lips, Wearing Earrings, and Young faces show only a minor effect
on the recognition performance. For both recognition models, the majority of
the investigated attributes strongly affect the recognition performance. Some
of the observations might be explainable.

  * •

Demographics: Recent works [33, 54, 28, 6] extensively discussed the impact of
demographic attributes on face recognition. Our results support the findings
from previous works. We observe an improved recognition performance for the
attributes Middle Aged, Senior, White, and Male. Contrarily, a degraded
recognition performance is observed for Young, Asian, Black, and Female faces.
For FaceNet, the observed differential outcome are stronger than for ArcFace.
Moreover, we could not show that Asian or Black faces perform weaker than
White faces on ArcFace, since the data unbalance lead to a low validity for
our results.

  * •

Visibility-related attributes: We observe that attributes that indicate a
fully visible face lead to an improved face recognition performance. This
includes the attributes Fully Visible Forehead, Receding Hairline, No Eyewear,
and Bald. In contrast, attributes that might lead to small partial occlusions
of the face lead to significantly degraded recognition performances [72]. For
FaceNet, this includes faces with an Obstructed Forehead, Bangs, and Wavy
Hair. For ArcFace, this includes samples with Eyeglasses or Bangs.

  * •

Temporary attributes: For faces with temporary attributes, such as for
accessories, a degraded face recognition performance can be observed. This
includes Wearing Hat, Wearing Earrings, Wearing Lipstick, and Eyeglasses.
Beside a partial-occlusion of small parts of the face, these attributes are
non-permanent and can quickly change the appearance of the face.

  * •

Anomalous characteristics: It turns out that conspicuous characteristics that
is only possessed by a small proportion of the population lead to strongly
enhanced recognition performances. This includes Arched Eyebrows, Big Nose,
Pointy Nose, Bushy Eyebrows, Double Chin, and High Cheekbones [66].

  * •

Facial expressions: Faces that are Smiling or that have their Mouth Closed
perform above average for face recognition. However, faces with non-neutral
expressions lead to degraded face recognition performances. This bias might
come from the data utilized for training that usually contains neutral or
smiling faces and was discussed in more details by previous works [10, 11].

While these attribute-dependent differential outcome might be explainable, the
reason for the impact of other attributes on recognition is currently unclear.

  * •

Colors: The results demonstrate strong differential outcome based on the
user’s hair- and eyecolor. For FaceNet, faces with Blond Hair, Black Hair, and
Brown Hair show strongly degraded recognition performances. In contrast, faces
with Gray Hair lead to an improved recognition. For ArcFace, Gray Hair also
strongly improves the recognition performance while Black Hair decreases it.
The differential outcome for Blond Hair and Brown Hair strongly varies
dependent on the used decision threshold. For instance, for high FMRs, Blond
Hair has a positive effect on recognition, for a lower FMR (e.g. $10^{-4}$)
the same attribute changes to a negative effect. The same can be observed for
eyecolors. Faces with Brown Eyes perform weaker than faces from the opposite
group. The differential outcome of these attributes does not reflect the
distribution of the training data and thus, might arise from a different
origin.

  * •

Beard: As we discussed before, attributes that might induce a partially
occluded face lead to a degraded face recognition performance. Although,
beards can cover parts of the face, the results demonstrate the faces with No
Beard perform below-average, while faces with e.g. a 5 o Clock Shadow achieve
much higher recognition rates.

  * •

Wearing Necktie: Unlike other accessories, Wearing Necktie improved the face
recognition performance drastically. We assume that this might results from a
data collection bias induced by the correlation with hidden factors, such as
environment. Persons who present themselves in public (e.g. celebrities) might
often wear a necktie and thus, photos are often taken with frontal poses and
full lightning. However, the high validity and the strong differential outcome
makes it hard to argue in this direction.

  * •

Antagonistic Behaviour: Some attributes might result in differential outcome
of the opposite direction depending on the used training-principle (triplet vs
angular margin loss). For instance, faces with Wavy Hair lead to a negative
performance on FaceNet and to positive performance on ArcFace. Also the
attributes Attractiveness, Heavy Makeup, and Oval Faces negatively affect the
recognition performance on FaceNet, but show some strong positive impacts on
the recognition performance of ArcFace.

As mentioned earlier, the resulting performance of a face recognition model is
mainly determined by its loss-function, its network architecture, and the
utilized training data. Since both investigated models have the last two
points in common, the observed differences in the performance might arise from
the underlying training principles. Generally, we observe that the large
angular margin loss from ArcFace leads to a significantly stronger overall
recognition performance compared to FaceNet. The loss aiming to enhance the
model robustness also shows a clearly visible effect on the attribute-related
differential outcome. On ArcFace, slightly less attributes negatively affect
the recognition performance than on FaceNet. However, the differential outcome
that origins from the affected (biased) attributes are still of high impact. A
remarkable observation is the fact that the differential outcome remain
relatively constant over several decision thresholds for FaceNet, while for
ArcFace the differential outcome often significantly vary for different
decision thresholds. This can be observed for instance for faces with Bangs,
Blond Hair, or a Double Chin.

####  IV-C3 Future challenges for face recognition

The observations of the experiment point out some critical issues of current
face recognition solutions in terms of robustness, fairness, and
explainability.

  * •

Need for robustness: Face recognition systems need to become more robust
against partial occlusions (from accessories or hair) [72, 42], facial
expressions (beyond neutral and smiling faces) [50], and temporary attributes
that might change the daily appearance of a face [64, 71]. This can greatly
enhance the applicability in more real-life scenarios.

  * •

Need for fairness: Face recognition systems need to enhance the user-fairness.
We observed differential outcome based on the user-demographics (demographic-
bias), anomalous characteristics (such as pointy noses, bushy eyebrows, and
high cheekbones), beard types, and accessories. This can lead to
discriminative decisions [56] of face recognition systems that several
political regulation, such as the GDPR [74], try to prevent.

  * •

Need for explainability: Face recognition models need to explain themselves.
Why do colors/face shapes/beards/accessories lead to differential outcome? Why
can we observe an antagonistic behaviour between the two different learning
principles for some attributes? In order to enhance the model transparency and
to enable efficient model-debugging, future work have to elaborate on the
explainability [5, 48] of face recognition models.

  * •

Need for comprehensive approaches and transfer learning: The previous areas
related to robustness, fairness, and explainability will significantly benefit
from more comprehensive approaches that consider simultaneously all the
elements and attributes in place [59, 20], exploiting at the same time
previous or general knowledge of the problem at hand [21, 61]. Most of the
research so far in biometrics bias, especially around face biometrics, has
been mainly oriented to studying individual elements (e.g., gender or
ethnicity) not exploiting previous models or evidence. There is a need for
more comprehensive approaches like the one presented here (incorporating
simultaneously 47 relevant attributes) and new schemes to easily exploit the
generated knowledge.

##  V Conclusion

The growing effect of face recognition systems on the daily life, including
critical decision-making processes, shows the need of non-discriminative face
recognition solutions. Previous works focused on estimating and mitigating
demographic-bias. However, to deploy non-discriminatory face recognition
systems, it is necessary to know which differential outcome appear in the
presences of certain facial attributes beyond demographics. Driven by this
need, we analysed the performance differences on two popular face recognition
models concerning 47 different attributes. The experiment was conducted on the
publicly available MAAD-Face database, a large-scale dataset with over 120M
attribute annotations of high-quality. To prevent misleading statements of
attribute biases, we consider attribute correlations and minimize the effect
of unbalanced testing data via control group based validity values. We
investigated the effect of two different learning-principles on the
differential outcome originating from facial attributes. The results show
that, besides demographics, many attributes strongly affect the recognition
performance of both investigated face recognition models, FaceNet and ArcFace.
While for FaceNet the observed differential outcome originated by several
attributes remain relatively constant, these differences strongly depend on
the used decision threshold for ArcFace. We provided explanations for many
observed performance differences. However, the reason for some observations
remain unclear and have to be addressed by future work. The findings of this
work strongly demonstrate the need for further advances in making face
recognition systems more robust, explainable, and fair. We hope these findings
lead to the development of more robust and unbiased face recognition
solutions.

##### Acknowledgment

This research work has been funded by the German Federal Ministry of Education
and Research and the Hessian Ministry of Higher Education, Research, Science
and the Arts within their joint support of the National Research Center for
Applied Cybersecurity ATHENE. This was also funded from the projects BIBECA
(RTI2018-101248-B-I00 MINECO/FEDER) and PRIMA (H2020-MSCA-ITN-2019-860315).

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