# Applying Artificial Intelligence for Age Estimation in Digital Forensic
Investigations

Thomas Grübl, Harjinder Singh Lallie<EMAIL_ADDRESS><EMAIL_ADDRESS>WMG, University of Warwick, Coventry, CV4 7AL

###### Abstract

The precise age estimation of child sexual abuse and exploitation (CSAE)
victims is one of the most significant digital forensic challenges.
Investigators often need to determine the age of victims by looking at images
and interpreting the sexual development stages and other human
characteristics. The main priority – safeguarding children – is often
negatively impacted by a huge forensic backlog, cognitive bias and the immense
psychological stress that this work can entail.

This paper evaluates existing facial image datasets and proposes a new dataset
tailored to the needs of similar digital forensic research contributions. This
small, diverse dataset of 0 to 20-year-old individuals contains 245 images and
is merged with 82 unique images from the FG-NET dataset, thus achieving a
total of 327 images with high image diversity and low age range density. The
new dataset is tested on the Deep EXpectation (DEX) algorithm pre-trained on
the IMDB-WIKI dataset.

The overall results for young adolescents aged 10 to 15 and older
adolescents/adults aged 16 to 20 are very encouraging - achieving MAEs as low
as 1.79, but also suggest that the accuracy for children aged 0 to 10 needs
further work. In order to determine the efficacy of the prototype, valuable
input of four digital forensic experts, including two forensic investigators,
has been taken into account to improve age estimation results. Further
research is required to extend datasets both concerning image density and the
equal distribution of factors such as gender and racial diversity.

###### keywords:

age estimation , digital forensics , CNN , child sexual abuse and exploitation
, facial image datasets

††journal: Journal Name

##  1 Introduction

One of the key challenges in the investigation of child sexual abuse and
exploitation (CSAE) material relates to the age estimation of both the abused
and the abuser. Humans can estimate several facets, including age, height,
weight, gender, ethnicity, and facial expressions reasonably easily. However,
human decision making in this area can be impacted by the size and volume of
images to be processed and also the trauma caused by the process of
assessment.

Of equal concern are cognitive biases presented to the investigator. These are
often induced by traumatic stress and other psychological disorders caused by
the regular examination and processing of this type of evidence [sanchez2019].
Distress and anxiety, or situations that create feelings of disgust can render
impaired decision-making [lerner2004] and negative emotions render risky
decisions [heilman2010]. Furthermore, human factors such as cognitive bias can
also impede effective decision-making [sunde2019]. The motivation for saving a
child can influence rational analyses [sunde2019]. In some cases, staff
exposure to disturbing material can lead to immense psychological stress
[jones2012].

Machine learning (ML) techniques can aid the development of Decision Support
Systems (DSS). ML can help to increase estimation accuracy but also reduce
human exposure time to CSAE material. Algorithms must be trained intensively
before they can achieve acceptable estimation accuracy [anda2018], however,
generally, whenever a suitable algorithm is trained with sufficient sample
images, it often outperforms the human judgements [anda2018, antipov2016,
escalera2016, rothe2015, rothe2016].

ML techniques can serve as complementary tools for forensic investigators.
These systems can aid in identifying, restoring and gathering different kinds
of evidence, processing the material via suitable reasoning modules (e.g. AI
classifiers), and presenting the results to an investigator for final decision
making [mukkamala2003, irons2014, costantini2019]. A number of ML techniques
have been proposed to provide accurate age predictions using facial images
[anand2017, antipov2016, chen2017, escalera2016, feng2019, rothe2015,
rothe2016]. However, ML techniques are rarely used in day to day
investigations. Perhaps one of the most promising techniques in the field of
ML are Convolutional Neural Networks (CNN), which are able to make predictions
about a particular input based on the training data they have received
beforehand. In the case of age estimation in digital forensics, CNNs are
trained with facial images of CSAE victims and the corresponding age labels.
Depending on the size, quality and diversity of the training dataset, the
neural network is able to output accurate age estimation results on completely
new testing images.

This study proposes a prototype that receives an input image and classifies
the image into an age category. This research will benefit both crime victims
as well as digital forensic investigators. Although a lot of further research
must be done in this field, the findings act as the first step to faster, more
accurate, less biased and less emotionally draining digital forensic
investigations.

##  2 Background

Current investigative techniques are steadily becoming obsolete, not only due
to advancements in technology but also a growth in cybercrime and limitations
in time and resources [irons2014]. To handle these challenges, intelligent
tools must be implemented, which help to tackle intricate problems.
Researchers have identified a number of important research challenges which
include:

  * •

Increasing demand to analyse the overwhelming bulk of evidence and resulting
in significant delays in the judicial process [anda2018, casey2009,
garfinkel2010, hoelz2009, peersman2016].

  * •

Lack of intelligent analytical approaches to investigations [atlam2020,
costantini2019, ganesh2017, irons2014, krivchenkov2019, peersman2016].

  * •

Staff exposure to disturbing material [bourke2014, powell2015, sanchez2019,
seigfried2018].

These problems are long-standing digital forensic challenges. Although
acknowledged for a while that the application of ML techniques can yield
improvements in digital forensic processes [beebe2009, irons2014], practical
research contributions in this field are still limited and the lack of diverse
approaches in the recent literature can be interpreted as a stagnation of the
research field and needs to be addressed immediately.

CSAE remains a high-volume offence, with an upsurge in recorded incidents
across the UK. Between 2017 and 2018, the number of reports of suspected CSAE
material in the UK has risen by over a third [nca2019]. Similarly, the number
of global reports to the National Center for Missing and Exploited Children
has risen by over 80%. The UK National Crime Agency reported that in 2019,
over 300,000 individuals in the UK alone were estimated to present a threat to
children and 7,600 children were safeguarded in relation to online CSAE
material. Between January 2015 and March 2019, over 8.3 million unique first-
generation images were added to the Child Abuse Image Database (CAID)
[nca2020]. From 2016 to 2017, the UK CSAE referrals bureau received, on
average, over 4000 referrals per month, which is ten times more than in 2010
[nca2017]. However, the true scale of CSAE is suspected to be much higher than
recorded in official statistics [nca2019].

Some progress has been made in the auto detection and categorisation of CSAE
material. These systems use hash algorithms to match seized images and videos
with pre-recognised images/videos. Hashes of suspect images are compared with
hashes stored in systems such as the Child Abuse Image Database (CAID111CAID
is the UK’s national child abuse image database that has been developed by the
Home Office in cooperation with the police and industry partners
[kloess2019].), which facilitates the identification of previously known CSAE
material [kloess2019]. Not only do the obtained images have to be bit-
identical, but images not matched with CAID have to be manually investigated
and even where images match, a selection must be manually validated to ensure
consistency.

The victim’s age plays a crucial role in the prosecution of CSAE cases.
Investigators must ascertain whether a subject – the victim or suspect – is a
child or an adult, and quite often an assessment is made of the approximate
age of the subject. The UK National Society for the Prevention of Cruelty to
Children [nspcc2020] and the UK Crown Prosecution Service [cps2018] define a
child as a person under the age of 18. The World Health Organisation (WHO)
defines children as “a person 10 years or younger” and adolescents as “a
person aged 10 to 19 years inclusive” [who2013]. kloess2019 provide a more
granular definition. They define the development stages of children as
follows: early childhood (1-6), later childhood (6-10), younger adolescent
(10-16) and older adolescent (14-17). kloess2019 definitions will be used
throughout since they comply with the NSPCC and CPS definitions.

###  2.1 Intelligent Forensics

The term intelligent forensics, originally coined by irons2014, refers to the
ability of computers to learn particular forensic tasks from data using
“intelligent” methods, such as Machine Learning (ML), to enhance digital
forensic processes. Notwithstanding a number of early studies acknowledging
the value of AI in digital forensic investigations, there have been very few
practical contributions and tools are still largely user-dependent. However,
more importantly, very little research has been published on the optimisation
of age estimation processes in DFIs.

peersman2016 present an image and video classification module for the
identification of CSAE content using automatic filename categorisation,
content classification (RGB-based skin detection) and hash-based detection.
Their results suggest an accurate tool for the detection of CSAE material with
false-positive rates of 7.9% for images and 4.3% for videos. This contribution
may benefit from the utilisation of newer hashing algorithms, and reducing
reliance on exact hash matching techniques given that cropping/cutting CSAE
materials will render a different hash.

There appears to be a lot of scepticism for AI-based systems [feldman2018,
tasioulas2019]. costantini2019 outlined that while forensic techniques, such
as DNA analysis, are broadly accepted and trusted, AI-based support systems
are still treated with mistrust and it may be difficult to convince the
involved parties of the applicability of such support systems.

Unfortunately, these approaches do not provide a solution for the fundamental
legal investigative problem: the estimation of a victim’s age. Despite the
importance of seizing and categorising CSAE material, the estimation of age
and subsequently, the identification of victims is the central concern in law
enforcement. Hence, intelligent forensic techniques for reducing the forensic
backlog, categorising images, standardising information representation are,
without a doubt, necessary and long overdue.

###  2.2 Human Factors in Digital Forensic Investigations

##### Cognitive Bias

Forensic experts do not always produce consistent results and are sometimes
prone to errors related to technological and human factors [sunde2019].
Technological errors include faulty timestamps, data loss, and errors related
to the lack of scientific data and research. Human factor related errors
include cognitive bias, intentional bias and inadvertent errors.

The sources of cognitive bias are strong emotions, such as anger, frustration
or confidence often due to the “underpinning of expertise” [dror2011].
Intentional bias and general errors are related to lack of motivation,
training or incompetence. sunde2019 suggest that potential countermeasures can
include psychological training, peer review or hypothesis-testing.

##### Psychological Stress

There is a growing body of research which outlines the psychological impact of
exposure to CSAE material. Investigators can develop traumatic stress caused
by constantly processing and examining CSAE material [sanchez2019]. powell2015
investigated the impact of observing CSAE material on 32 child exploitation
investigators and found that this activity contributed to salient emotional,
psychological, social and behavioural consequences. Similar findings have been
reported in a study of 129 law enforcement officers by seigfried2018. The
results showed that contact with CSAE material cases leads to low self-esteem,
impaired concentration compared to other uninvolved investigators, and/or
Secondary Traumatic Stress (STS) which relates to symptoms which occur from
observing or learning about a traumatic event [bourke2014].

###  2.3 Age Estimation Techniques

While face recognition has been extensively studied [mathias2014, parkhi2015,
rowley1998, turk1991, viola2004], automatic and precise age estimation is
still a comparably new research field. Recent deep learning age estimation
techniques [anand2017, antipov2016, chen2017, escalera2015, liu2017,
rothe2015], which have built upon early work on exact age estimation [fu2010,
guo2012, lanitis2002, lanitis2004], have shown promising results.

Recently, there has been an increase in studies that leverage deep neural
networks for face detection, classification of gender and age estimation from
facial images [gonzalez2018]. From simple examples, which use 3-layer neural
networks [agarwal2010], to other more sophisticated ones, which use multiple
neural networks [dong2016] and/or networks that potentially consist of
hundreds of layers [huang2016], have also been used successfully by numerous
researchers.

Facial appearance is an essential trait in estimating human age [geng2013].
Humans can usually accurately determine facial information such as identity,
gender and race; however, the estimation of age is more challenging.

Recent progress in this field can be ascribed to the developments in the area
of deep learning and the availability of large scale datasets [parkhi2015].
Face recognition has benefited immensely from comprehensive and high-quality
datasets, such as Labeled Faces in the Wild (LFW) and YouTube faces
[eidinger2014]. However, facial image datasets such as LFW cannot be
considered for age estimation tasks due to missing age labels.

####  2.3.1 Facial Image Datasets

Table 1: Comparison of different facial image datasets.

Literary source | Dataset | Subjects (♀) | Images (♀) | Age |  $\leq$ 16 |  $\leq$ 25 | Max gap | P | A
---|---|---|---|---|---|---|---|---|---
ricanek2006 | MORPH Album 1 | 515(95) | 1724(294) | N/A | N/A | N/A | 29 | Y | Y
ricanek2006 | MORPH Album 2 | 13618(2159) | 55608(8551) | 16-77 | N/A | N/A | 5 | Y | Y
lanitis2015 | FG-NET | 82(34) | 1002(395) | 0-69 | 61.07 | 80.94 | 45 | Y | Y
chen2014 | CACD | 2000 | 163446 | 16-62 | N/A | N/A | 10 | Y | Y
liu2016 | CAFE | 901 | 4659 | N/A | N/A | 58.04 | N/A | Y | Y
bianco2017 | LAP | 1010 | 3828 | N/A | N/A | N/A | 80 | Y | Y
rothe2015 | IMDB-WIKI | 22741 | 524230 | N/A | N/A | N/A | N/A | Y | Y
escalera2015 | LAP | N/A | 4699 | N/A | N/A | N/A | N/A | Y | N
deb2018 | CLF | 919(315) | 3682 | 2-18 | N/A | 100 | 6 | N | N
grd2016 | ageCFBP | 287(151) | 1655 | 0-25 | 40 | 100 | N/A | N | Y
founds2011 | MEDS | 518(52) | 1309 | 15-69 | N/A | 34.37 | N/A | N | Y
eidinger2014 | OUI-Adience | 2284 | 26580(12826) | 0-60 | N/A | 38,67 | N/A | Y | Y
fu2008 | UIUC-IFP-Y | 1600(800) | 8000(4000) | 0-93 | N/A | N/A | N/A | N | Y
gupta2012 | FSAR | N/A | 432 | N/A | N/A | N/A | N/A | Y | N
choi2011 | BERC | 390 | 390 | 3-83 | N/A | N/A | N/A | N | Y
somanath2011 | VADANA | 43(17) | 2298 | 0-78 | N/A | N/A | N/A | Y | Y
♀$\,\to\,$ female; P $\,\to\,$ Publicly available. A $\,\to\,$ Applicable to age estimation research |  |

One of the most pressing issues in facial age estimation is the lack of
sufficient training data for many different age groups [rothe2016]. Because
age estimation methods commonly depend on a process of learning and training,
the selection of the right training data is a crucial step that defines the
subsequent performance of a classifier [gonzalez2018]. In the case of the age
estimation of CSAE victims, a well-trained neural network can still be
inaccurate if trained with the wrong age groups.

One of the oldest and largest databases are the MORPH albums which consist of
1724 and 55608 images, respectively, and can be mainly used for age-gap
verification (Figure 1). MORPH Album 2 has a small age-gap issue compared to
Album 1, which could pose a problem for the age gap verification accuracy.

The Face and Gesture Recognition Network (FG-NET) database was published in
2004 and is a valuable and important contribution to the field. The dataset
contains 1002 images from 82 different individuals with ages ranging between 0
to 69. Approximately 61% of the images show subjects that are younger than 16
and about 81% are younger than 25. The publisher’s aim, was to contribute to
research technology development in the field of face recognition by providing
a publicly available dataset of facial images. However, one limitation of the
FG-NET database is the lack of racial diversity and the relatively small size.

rothe2015 proposed a new method called Deep Expectation (DEX) and crawled over
500,000 celebrity images from both Wikipedia and IMDB to produce a database of
images annotated with age and gender labels. rothe2015, rothe2016 use a model
with 101 output neurons, including age ranges of 0 to 100. The IMDB-WIKI
dataset benefits from containing a high number of images. Unfortunately, the
dataset suffers from a lack of training images (training density) for low age
ranges, i.e. it is inaccurate and therefore unhelpful in facial age estimation
of very young children. However, rothe2015 achieve low MAEs in estimating the
ages of mid-to-late teenagers, which is particularly important when attempting
to distinguish jurisdictional boundaries, for example, distinguishing CSAE
material from adult pornography. Furthermore, the authors do not explicitly
employ facial landmarks, which, as they state, could improve the performance
of DEX. Facial landmarks are fiducial facial key points that are used to
analyse facial images. Facial landmark detection algorithms automatically
identify the locations of eyes, nose, mouth, wrinkles and other unique
features [wu2019facial]. Yet, even without relying on landmarks and by
handling small occlusions robustly, DEX significantly outperforms the human
reference on facial age estimation of adults.

Table 1 shows a detailed list of facial image datasets for two different
purposes – age estimation and age-gap verification. Figure 1 shows the purpose
of each dataset and the different factors of diversity. Although racial
diversity would benefit the overall accuracies of age estimation, it cannot be
considered as a diversity factor due to the limited information about race
distributions in publications in this domain. Hence, the diversity factor is
calculated by considering the gender and age distributions.

grd2016 acknowledged that the accuracy of child age estimation suffers from
lowered accuracies due to the lack of training data for these specific age
groups. The majority of available datasets do not offer images of individuals
from birth to adulthood.

grd2016 chose to manually collect personal data of minors, instead of crawling
publicly available data from the internet. This method requires parental
consent, involves fundamental legal considerations and is not scalable,
whereas related studies such as the research from antipov2016 and liu2016
apply automated data crawling methods to enhance the efficiency of the image
collection process. The datasets of both antipov2016 and grd2016 have remained
private.

The previous studies reveal that the lack of facial images of children poses a
big problem for the accuracy of age estimation techniques, which could be
applied to DFIs. As huang2008 conclude, training and testing face recognition
and age estimation algorithms on highly diverse sets of faces is crucial to
achieving the best possible results. Hence, the value of studying the above
areas of literature will support this research on developing perfectly
tailored datasets for juvenile age estimation.

Figure 1: Bubble diagram of facial image datasets showing the name, size,
year, diversity and purpose.

####  2.3.2 Age Estimation Using Neural Networks

The estimation of a person’s age can be achieved using many different methods.
This section focuses specifically on neural networks (NNs). A neural network
is a neuron-connectivity pattern inspired by the neural structure of mammals
[zhang2019]. Neural networks firstly proved their ability in object
recognition in the ImageNet competition [krizhevsky2012]. They can be used for
many different tasks that involve learning from images, such as medical image
analysis, exchange rate predictions but also age estimation. From a high-level
perspective, age estimation methods can be divided into machine learning
approaches based on feature sets and deep learning pattern recognition
techniques [anand2017].

##### Classification and Regression

Age estimation can be seen as a classification or regression problem
[chen2017]. In an age classification task, a facial image is assigned to a
particular group by a multi-class classification algorithm. A regression task
outputs a discrete age estimation value [dong2016].

rothe2015 argue that age estimation is a piece-wise regression problem, since
age is a continuous variable, although they still train convolutional neural
networks (CNNs) for classification into 101 year labels [0…100]. When training
for classification, it is vital that each age range covers the same number of
years and is compromised of the same number of training images [rothe2016].

Classification models include Support Vector Machines (SVM), CNNs, DNNs,
Nearest Neighbours, whereas common regression methods include Support Vector
Regression (SVR) and Quadratic Regression (QR).

##### Convolutional Neural Networks

Deep CNNs have shown promising results for computer vision tasks on images
[park2016]. Recently, many studies have employed CNNs to predict the age of an
individual from a single facial image [anand2017, antipov2016, chen2017,
feng2019, rothe2015]. Their methodology is generally identical; by combining a
series of convolutional, pooling and fully or partially connected layers as
well as activation functions, the final output value is calculated (see Figure
5).

CNNs apply, as the name suggests, discrete convolution (1) to extract a set of
local features from a facial image. A convolutional layer performs the
mathematical convolution over the matrices of preceding layers in order to
extract facial features from the image. Facial features can be divided into
high-level, medium-level and low-level features which respectively describe
the facial structure, more detailed features (eyes, ears, nose, mouth) and
very detailed features (edges, dark spots, wrinkles) [liu2015].

$(f\ast g)(t)=\sum_{\tau=-\infty}^{\infty}f(\tau)g(t-\tau)$ (1)

Equation (1) describes the sum of the mathematical products of two (shifted)
functions $f(t)$ and $g(t)$ for every point $\tau$ in time. Convolution in
digital image processing is performed with a filter array (filter matrix).
This filter array and every shifted version of the array are multiplied with
the original image. The result is a matrix that describes an extracted
feature.

The pooling layers are responsible for down-sampling the image and thus
reducing the complexity. This process can be viewed as decreasing the
resolution of an image. Max-pooling is one method that is commonly used within
CNNs and refers to summarising the distinct regions of an image to a single
value. Generally, a 2 $\times$ 2 pixel region is combined and the highest
pixel value is forwarded to the next layer [albawi2017]. krizhevsky2012 were
using an overlapping pooling scheme for their famous ImageNet classification.
This method, compared to non-overlapping pooling, can reduce error rates by
overlapping the grids with one another. Another advantage observed by the
authors is that overlapping pooling layers are slightly reducing the
probability of overfitting, which CNNs are generally prone to.

A fully-connected layer, as the name suggests, directly connects each neuron
in a layer to every single neuron in another layer. In order to redirect the
matrix output of the convolutional and pooling layers into a dense layer, the
layer needs to be flattened first. They are commonly placed at the rear end of
a CNN to produce the final classification decision [dung2019]. A disadvantage
of fully-connected layers is the high computational complexity compared to
partially-connected layers. Hence, many modern networks are limiting the
number of fully-connected layers to a minimum (including the VGG-16 CNN used
in this research).

Every convolutional or fully-connected layer usually feeds the output into the
Rectified Linear Unit (ReLU) non-linearity function [krizhevsky2012]. To
achieve non-linearity and therefore adjust the output values, different
mathematical functions such as tanh, sigmoid, or ReLU can be applied. The
latter is the most commonly used in today’s CNNs since it is more
computationally efficient than other functions. The ReLU ensures that all
values that are $\leq 0$ are becoming 0 and the remaining values stay in a 1:1
proportion.

When training a network, each iteration optimises the network by back-
propagating the learned information. The process of forward-propagation is
merely feeding the input images into the network and calculating a variable in
the output layer. However, the weights in a network need to be adjusted
accordingly based on the accuracy of the output layer. Hence, the back-
propagation of the errors is necessary. The error value is determined by
calculating the difference between the expected value and the actual output
value [shen2016].

###  2.4 Summary

The background outlined herein highlights the need for the creation of more
tailored datasets for digital forensic investigations. Several datasets could
be more applicable to DFIs if the creators paid close attention to image
diversity, especially as this factor is crucial for training deep neural
networks.

Further research is required to gain a better understanding of the most
effective facial image dataset composition for achieving more accurate age
estimation results on CSAE images. The next stage of this research will
empirically evaluate the accuracy of an existing algorithm and determine the
efficacy of the prototype.

##  3 Methodology

An age estimation prototype was developed and evaluated using statistical
metrics to assess the accuracy of the prototype and compare the results to
existing age estimation solutions. Additionally, we collected and analysed
qualitative responses to determine the efficacy and usefulness of the approach
to digital forensic practitioners. This was intended to help identify further
improvements to the age estimation prototype.

###  3.1 Framework for the Development of a Prototype

The availability of adequate datasets is a key challenge in this research
area. There are a number of strictures which prevent the gathering of such
data. Children in the UK can give their own consent under the GDPR at the age
of 13. Any child younger than that relies on parental consent [ico2020].

The background section revealed that there are existing datasets that can be
considered for this research. These datasets can be used to both serve as
input data for training new models or fine-tuning pre-trained models. The
large age estimation dataset “IMDB-WIKI” was chosen to serve as a basis of the
age estimation process.

We adopted the approach demonstrated by [lanitis2004] and [liu2016] and merged
the dataset from [lanitis2004] and the newly collected dataset. This method
has been selected because (a) no legal issues arise when collecting public
images222As long as the dataset is not made public. If we decide to publish
the collected dataset, legal factors such as copyright must be taken into
account. and (b) several existing datasets have pertinent quality and quantity
that fulfils the requirements of this research.

However, some challenges arise with this approach, such as potential
redundancies or missing age labels. Redundant images can hardly be identified
because the image hashes are almost always different for the newly collected
dataset (due to crop sizes). Furthermore, missing age labels cannot be
reconstructed; hence some images might need to be discarded.

The newly collected dataset contained some redundancies that were removed
based on the filename structure firstname_lastname_age.filetype to end up with
one image per individual. The same approach has been applied to the 1002-image
FG-NET dataset.

###  3.2 Testing the Prototype Accuracy

####  3.2.1 Statistical Metrics

In order to quantitatively analyse the accuracy of the prototype, we applied
selected statistical methods.

Perhaps the most popular metric for evaluating systems of age estimation is
the Mean Absolute Error (MAE), which is defined as a “mean value of absolute
differences between predicted ages x-hat and real (biological) ages”
[antipov2016]. The MAE (2) is the industry-standard measure for age prediction
errors [anand2017, antipov2016, chen2017, rothe2015, rothe2016]. The
measurement can be described as follows:

$MAE=\dfrac{1}{N}\sum_{i=1}^{N}|\hat{x_{i}}-x|$ (2)

In addition to the MAE, many researchers also adopt the Cumulative Score (CS)
as a performance measure [chen2017, han2013, weng2013]. The CS (3) indicates
the percentage of images for which the error is less than a certain number of
years [han2013]. The higher the CS, the more accurate is the age estimation
system. The CS measurement can be implemented by either defining an absolute
age range (e.g. ±2 years) or a neighbouring age range (e.g. the two
neighbouring age classes). This measurement can be described as follows:

$CS(l)=\dfrac{M_{e\leq l}}{M}$ (3)

Another commonly used metric is the $\epsilon$-error (4). This quantitative
measure can be used for datasets where there is no ground truth age, but
instead, a group of people vote for the age label. The error measures the
standard deviation $\sigma$ of the guessed age. The final $\epsilon$-error
represents the average over all images, 0 being a perfect prediction and 1
being a wrong prediction [rothe2016]. This measurement can be described as
follows:

$\epsilon=1-e^{-\frac{(x-\mu)^{2}}{2\sigma^{2}}}$ (4)

MAE and CS were applied in this research. The $\epsilon$-error was not used
because no apparent age estimation has been carried out. However, this metric
could be appropriate for future research in this area.

The MAE was used to evaluate the absolute accuracy of the prototype. A small
MAE means that the estimated age is close to the real age and thus a
reasonable estimate. On the other hand, a high MAE value indicates inaccurate
results. The CS was used to evaluate the overall performance of the age
estimation prototype within a particular age interval. The higher the CS
measurement, the more accurate is the age estimation system.

In general, the validity and reliability of accuracy evaluation methods can be
ensured by correlating the different statistical methods and thus proving the
trustworthiness of the results. By combining the MAE and the CS, a high level
of quality concerning the consistency of the results can be achieved.

###  3.3 Testing the Prototype Efficacy

The researchers were mindful that this was one of the first contributions in
this area and that it was important to determine the value this might add to
an investigation, thereby motivating further development in this field. To
this effect, qualitative interviews were conducted with two investigators from
West Midlands Police and two academic researchers in order to aid our
understanding of the efficacy of our approach.

Table 2: Interview participant information. Ref |  Role |  Department |  Years of Experience
---|---|---|---
A1 |  Higher Forensic Analyst |  Digital Forensic Unit |  14
A2 |  Higher Digital Forensic Officer |  Digital Forensic Unit |  4
B |  Director of Research and Development |  Forensic Research |  10
C |  Principal Lecturer |  School of Law/Policing/Social Sciences |  15

The interview transcripts were transcribed using manual intelligent verbatim
transcription and narrative content analysis was performed subsequently.
Narrative content analysis is commonly used to underline common themes between
different participants and is necessary to get initial insights into the
qualitative data.

###  3.4 Limitations and Potential Problems

As with the majority of studies, this research is also subject to limitations
and potential problems that arise from both limited access to data and time
constraints. These constraints relate to racial diversity in datasets and
reliability issues.

The failure to test the prototype on a more diverse dataset could lead to
racial bias due to unequal distribution of images. Future research could
mitigate this problem by collecting an image dataset with a strong focus on
the equal distribution of factors such as origin and skin colour of the
subjects.

With more resources and time, the data collection process may be further
optimised by developing an automated dataset generator that selects the most
useful images from various existing datasets based on parameters such as age,
race and image quality. This could benefit the study with regard to a more
precise data distribution and a better overview of the correlation between a
particular set of input images and the outcome, i.e. the estimation accuracy.

Figure 2: IMDB-WIKI age distribution function [rothe2016].

Figure 3: FG-NET age distribution function.

The image preprocessing stage might be subject to reliability issues. Due to
time constraints of the study, we cannot accurately determine the perfect crop
size and rotation angle for every image to achieve the best possible
estimation results. Thus, the CNN accuracy might deviate slightly from the
best possible accuracy. To mitigate this issue, future research could
incorporate a more sophisticated image preprocessing tool.

##  4 Experiment Design

The experiment tests the preliminary stage of an age estimation prototype that
can be deployed in DFIs. Furthermore, the experiment includes a performance
analysis of a pre-trained CNN (with the IMDB-WIKI dataset) and attempts to
show how this CNN is performing on the newly collected dataset.

The main features of the prototype can be described as follows:

  1. 1.

Take an input image in any image format (.jpg, .png, .bmp, …).

  2. 2.

Classify the input image into an age category pre-defined by the CNN (0-100
years, one for each year).

  3. 3.

Print out a single number age estimation result within the range 0-100.

Table 3: Required Python packages. Package |  Version |  Description
---|---|---
Pillow |  7.0.0 |  Python Imaging Library fork.
PyWavelets |  1.1.1 |  Open source wavelet transform software.
cycler |  0.10.0 |  Composable style cycles.
decorator |  4.4.2 |  Signature-preserving function decorators.
imageio |  2.8.0 |  Read and write image data.
kiwisolver |  1.1.0 |  Cassowary constraint solving algorithm.
matplotlib |  3.2.1 |  Creating static, animated, interactive visualisations.
networkx |  2.4 |  Create and manipulate complex networks.
numpy |  1.18.2 |  Package for scientific computing.
pip |  19.0.3 |  Package installer for Python.
protobuf |  3.11.3 |  Package for Google’s data interchange format
pyparsing |  2.4.6 |  Create and execute simple grammars.
python-dateutil |  2.8.1 |  Extends the standard datetime module.
scikit-image |  0.16.2 |  Collection of algorithms for image processing.
scipy |  1.4.1 |  Software for mathematics, science, and engineering
setuptools |  40.8.0 |  Package for facilitating packaging Python projects.
six |  1.14.0 |  Helps smoothing differences between Python versions.

###  4.1 Choice of Technology

The Debian 10.3 Linux distribution was used to set up the development
environment. The Caffe deep learning framework has several dependencies (Table
3). In addition, multiple libraries are needed to process input images,
present the data and build a GUI, the rothe2015 pre-trained Caffe model for
real age estimation was used. The model “contains” weights adjusted to over
500,000 training images. Hence, using this Caffe model is a considerable time-
saving.

####  4.1.1 Datasets

Figure 4: Preliminary graphical user interface of the age estimation
prototype.

The characteristics of the IMDB-WIKI dataset have already been laid out in
detail in the Background section. However, an important detail that helps with
the interpretation of the results in the subsequent chapter is the
distribution of age within the IMDB-WIKI dataset as shown in Figure 3. This
plot gives insights into the age density of images. The testing data consists
of the newly collected dataset of 245 images and an existing dataset of 82
images. Images from the FG-NET dataset (fourth highest diversity score) have
been merged with the new dataset in order to achieve a high-quality testing
set (Figure 3). Since the FG-NET dataset contains images from 82 unique
individuals, one image per person has been randomly selected where the person
depicted is 20 years old or younger.

Figure 5: IMDB-WIKI VGG-16 Convolutional Neural Network architecture (split
into six parts).

####  4.1.2 Data Processing

Figure 6: High-level software component diagram of the deep learning age
estimation prototype.

The structure of the deep learning framework is presented as a high-level
software component diagram (Figure 6). Each component can be interpreted as a
higher abstraction than a class. The VGG-16 architecture and the built-in 16
convolutional weight layers, is a convolutional network for large-scale image
and video recognition. The Caffe deep learning framework was developed by
jia2014 with the Berkeley AI Research team and is one of the most widely used
frameworks for image classification.

The proposed model architecture, consisting of the pre-trained model and
testing dataset, has two main advantages over traditional training methods:
(a) simplicity and low computational cost, because Caffe is among the fastest
CNN implementations available [jia2014] and (b) the likelihood of overfitting
in future training processes is reduced by adopting a well-balanced pre-
trained model. The main reason why the Caffe framework has been selected over
other platforms such as Tensorflow or PyTorch is that the pre-trained model is
readily available as a Caffe model. Furthermore, Caffe provides the option of
simply converting the model into Tensorflow. The image preprocessing has been
conducted using the rothe2015 face extraction Matlab script. However, since
many images were not cropped accordingly, the image preprocessing stage also
involves manual crops and resizes.

####  4.1.3 Network Architecture

This experiment uses a CNN with a VGG-16 architecture [rothe2016]. It has a
deeper architecture than its predecessors with 16 layers in total (13
convolutional and three fully-connected)333A more detailed description of the
different CNN components can be found in the Background section.. A unique
characteristic of this network is its relatively small convolutional filters
with a size of 3$\times$3 pixels. This enables the net to capture more
superficial geometrical structures rather than more complex ones. Lastly, the
expected value is computed by applying the Softmax normalisation function to
the output probabilities of the 101 neurons (one for each age class).

The network architecture is presented in Figure 5. The network was created by
running the draw_net.py script that is integrated into the Caffe framework:

    python3 CAFFE\_DIRECTORY/python/draw_net.py \
    MODEL\_DIRECTORY/age.prototxt \
    OUTPUT\_DIRECTORY/network.png

###  4.2 Design and Development

The Caffe model and prototxt file were imported using the Python function
estimate_age(). This function represents the core logic of the estimation
prototype and includes loading the image, image processing and computing the
output values.

The prototype does not only provide an estimated age integer value but also a
probability distribution function, which was plotted using matplotlib. This
graph, shown in Figure 8, represents each age class (0-100) on the x-axis and
the associated probability value (0-1) on the y-axis.

##  5 Results and Analysis

This chapter outlines the quantitative results from the evaluation of the pre-
trained Caffe model on the new testing set. First, the age estimation results
of individuals aged 0 to 20 are presented. Secondly, the corresponding MAEs
and cumulative scores are reported. Lastly, the accuracy of the prototype is
evaluated and analysed.

###  5.1 Evaluation of Results

The results are displayed as single-number age estimation values that
represent the output of the age estimation prototype.

From a high-level perspective, results are varying across different age
classes and from image to image. The obtained results underline the fact that
the pre-trained model has a low image density in the lower age ranges (0-16).
This model characteristic is responsible for most of the inaccurate
classifications. The notably large errors can also partly be traced back to
insufficient image quality and facial rotations that cannot be eliminated
during the image preprocessing phase. A part of the dataset was purposely
filled with imperfect images to adequately reflect genuine DFIs. In the area
of age estimation, imperfect images include facial rotations, partial
occlusions, zoomed faces, blurry images and any other characteristic of an
image that can reduce the accuracy of age estimation software [elmahmudi2019].

Figure 7: Example of face images in the new testing dataset.

Another striking trend can be observed that the majority of estimations is
higher than the real age, which can be related to the high image density in
the IMDB-WIKI dataset in the [30; 40] age interval. The so-called estimation-
shift is a common phenomenon amongst deep learning models that lack training
images in a certain range. This trend is especially prevalent amongst the
children in the age class (0-10) and younger adolescents (10-16). However, the
lower age ranges are not particularly relevant for DFIs because investigators
are generally able to estimate the age of children correctly.

It can also be observed that images that are classified with a small error of
$\pm$ 2 years are often within the second or third “guess” of the neural
network. So, for example, when a 16-year-old is estimated to be 15 years old
(Figure 8), it is common that the real age class is within the three highest
probabilities in the age probability distribution function. The red bar
represents the wrongly calculated age of 15, the green bar shows the real age.

The findings also show that there is a correlation between the estimation
accuracy and the distribution of the age probability curve. Results that are
relatively inaccurate, such as the age estimation of very young children,
often result in a flat distribution, i.e. a probability curve that is evenly
distributed. This outcome reflects the “indecisiveness” of the CNN. On the
other hand, accurate results usually produce a steep age distribution curve,
as shown in Figure 8.

###  5.2 Performance Analysis

As Figure 11 depicts, the MAE between age ranges differs greatly. Estimations
in the lower age ranges, in particular, are significantly deviating from the
ideal estimation line. One can suggest that the low image density of the pre-
trained model in the age range [0; 20] is likely a contributing factor to the
high MAEs shown in Table 5.2 and Figure 11. The proportion of images in the
age range [0; 7] that are correctly classified is 0%. This indicates that
images in this age range are scarce in the training dataset and thus the
neural network does not know how to cope with this kind of input data. The MAE
peaked at x = 0 years old with a value of 14.00 and x = 17 accounts for the
minimum MAE of 1.79. Although the plot shows MAEs ranging into the sub-zero
range, estimations smaller than 0 are not possible. This is merely a way of
presenting the absolute value of the MAE.

Figure 8: Age distribution example.

These results are particularly advantageous since digital forensic
investigators struggle the most with images depicting younger and older
adolescents (10-17), based on the interviews with digital forensic
investigators. In many cases, investigators are able to correctly estimate the
age of children, whereas the age estimation prototype makes mostly inaccurate
decisions in this range. The MAE is decreasing around the age of consent
boundary and thus the estimation accuracy is increasing in this critical range
for investigators.

Figure 9: Estimated vs real age of exemplar images. Table 4: Mean Absolute Errors and Cumulative Scores per age class. Age Class |  MAE |  CS=1 |  CS=2 |  CS=3
---|---|---|---|---
0 |  14.0 |  0 |  0 |  0
1 |  8.73 |  0 |  0 |  0
2 |  8.68 |  0 |  0 |  0
3 |  8.42 |  0 |  0 |  0
4 |  6.94 |  0 |  0 |  0
5 |  7.0 |  0 |  0 |  0
6 |  5.17 |  0 |  0 |  0
7 |  3.58 |  0 |  25 |  66.66
8 |  3.15 |  15.38 |  53.85 |  53.85
9 |  5.0 |  23.08 |  23.08 |  30.77
10 |  3.17 |  25 |  41.66 |  75
11 |  2.8 |  26.66 |  73.33 |  73.33
12 |  1.86 |  64.28 |  71.43 |  85.71
13 |  1.92 |  53.85 |  69.23 |  69.23
14 |  3.12 |  47.06 |  58.82 |  85.71
15 |  2.72 |  16.66 |  38.88 |  77.77
16 |  2.45 |  65 |  70 |  80
17 |  1.79 |  52.63 |  84.21 |  89.47
18 |  2.32 |  45.45 |  63.64 |  81.82
19 |  3.22 |  22.23 |  38.89 |  50
20 |  2.71 |  33.33 |  52.38 |  57.14

Figure 10: Age estimation results including the Mean Absolute Error.

Figure 11: Cumulative Score of 1 per age class.

Since age estimation needs to be accurate, especially near the age of consent
boundary, the researcher decided to define a stringent CS threshold: the two
neighbouring age classes. Any estimated result that lies within that range of
$\pm$ 1 year, gets counted as an accurate result. The results of the CS are
presented in Figure 11. It can be observed that the CS increases with the age
of the depicted individual and, therefore, correlates with the MAE. The
performance can be summarised as follows: The greater the integral of the CS
function over a particular set of age classes, the higher the accuracy within
that age range. The CS has been further divided into two additional sub
metrics: CS scores with an interval range of 2 years (Figure 13) and 3 years
(Figure 13) respectively. This approach was inspired by multiple digital
forensics experts that have been interviewed in the course of this research
(see Discussion and Conclusion). The argument behind that approach is that a
single CS cannot show the full picture of classification results. Three
different CS intervals provide digital forensic investigators with enhanced
intelligence about a set of images depicting the same individual. This can be
particularly important when images are imperfect.

Figure 12: Cumulative Score of 2 per age class.

Figure 13: Cumulative Score of 3 per age class.

Another factor that can negatively impact the CS values is over- and
underfitting the model. Since the pre-trained model in this experiment is
likely to be overfitted on adults, overfitting can be a contributing factor to
low CS values and high MAEs in specific age ranges. Underfitting, on the other
hand, would be indicated by a high bias and thus a large difference between
training and testing set.

###  5.3 Comparison with Recent Results in the Literature

In comparison, rothe2016 have used their IMDB-WIKI as a training set and,
inter alia, the FG-NET as a testing set. The FG-NET, as well as the MORPH
dataset, are used as their standard benchmark. Their DEX method achieved a
remarkable MAE of only 3.09 on the FG-NET with fine-tuning and 4.63 without
fine-tuning. wang2015 achieved an MAE of 4.26 on the FG-NET dataset, whereas
chen2013 had attained an MAE of 4.67. However, since the FG-NET dataset
contains 1002 images but only 82 distinct individuals, chen2013, rothe2016 and
wang2015 were effectively testing their model on only 82 individuals. This
implies that their testing set is biased on 82 individuals, which result in
poor testing quality. In contrast, this research is testing the model with 327
individuals and with an (almost) evenly distributed age range from 0 to 20-
year-olds. It is also fair to assume that the MAE in the age range [0; 20] was
slightly higher, since the estimation results for older individuals are more
precise due to the age distribution of the IMDB-WIKI pre-trained model. The
analysis and comparison of computational time is not particularly relevant for
this research because an existing pre-trained model was used and the model was
run in a VM on the CPU. Other authors have heavily focused on computational
time and algorithmic performance [anand2017], and therefore the results cannot
be compared to these publications. The findings reveal that the age estimation
performance is varying across different age groups. In particular, images
depicting children and young adolescents are suffering from high MAE and low
CS. Although this suggests that the model performance is inadequate, the poor
results in this age range can be partly traced back to the overfitting of the
facial image dataset of the pre-trained model and the diversity of the testing
dataset with regard to image quality, facial rotations and partial occlusions.
The results also show that the age estimation performance is increasing around
the age of consent boundary, which is especially important for digital
forensic investigations. Some age ranges are even showing lower MAEs compared
to the results in recent literature. However, theses MAEs are calculated over
an entire dataset and not only a specific age range.

##  6 Discussion and Conclusions

From a quantitative perspective, the findings suggest that even the most
diverse datasets can reveal accuracy issues in certain age groups if not
enough adequate training input is provided. For instance, since images
depicting five-year-old children are very scarce in the used training dataset,
the neural network is not able to process this kind of input accordingly.
Hence, the achieved results stress the need for more gender and racial
diversity as well as an increased image density in the lower age ranges. This
study’s conclusion agrees with recent and influential literature in the field
[bessinger2016, merler2019, ryu2018, ryu2017]. Since every face is different
and reflects variations in, inter alia, race, ethnicity, gender, age and
geography, neural networks can be biased in favour of external
characteristics. However, the accuracy of a neural network should not vary
because of these factors and, therefore, close attention needs to be paid to
developing balanced datasets that fairly represent the target group. While
there are differences in the experts’ backgrounds and fields, the results of
the software evaluation show that all interview participants are satisfied
with the efficacy of the prototype in the following areas:

  1. 1.

Presenting the age value as the key piece of information and supporting it
with an age probability distribution graph.

  2. 2.

Providing additional information about the image itself in the form of
metadata.

  3. 3.

Using three different cumulative scores to determine the efficacy of the age
estimation prototype.

The first area is a crucial piece of information since digital forensic
investigators do not support the idea of solely presenting crisp age values.
An age range is a better expression of accuracy and confidence intervals,
which is also consistent with the presentation of results in the literature
[anda2018, chen2017, dong2016]. Moreover, an often overlooked aspect is the
persuasiveness in court. For instance, if a digital forensic investigator in a
child abuse case is presenting evidence in court, they are never just relying
on a single age value and, therefore, a DSS such as an age estimation software
should present the results in a similar way. Furthermore, the analysis of the
narrative revealed that all four interview participants agree on the provision
of additional information in the form of metadata. Most age estimation
solutions today are displaying valuable information such as timestamps and
file sizes in order to correlate different pieces of evidence with one
another. However, participant A1, A2, and B also point out that a functioning
age estimation prototype needs to have the ability to classify a group of
images simultaneously and participants B and C stressed the need for an
increased age range in the dataset. These suggestions are perfectly reasonable
as a DFI usually comprises several gigabytes if not terabytes of image and
video material and age classification is required for every single case.
Increasing the age ranges of this particular research can be easily achieved
by merging additional datasets into the existing one if needed. Since many
facial image datasets [bianco2017, chen2014, liu2016, ricanek2006] include a
reasonable amount of images depicting individuals between the ages of 20 and
30, existing datasets can be comfortably extended.

###  6.1 Enhancing Digital Forensic Investigations

The interview participants A1 and A2 give the most comprehensive feedback on
both the technical and non-technical aspects of this research. The main
problems they have identified and how these issues can be addressed are
presented in Figure 14. One main suggestion is to include black and white as
well as sepia-style images into the dataset since digital forensic
investigators are often seizing images with colour filters. However, mixing up
coloured and clean-cut images with filtered ones can reverse the positive
effects of a racially diverse dataset. By including unnatural images, the
neural network could adjust its weights in favour of black and white images or
vice versa if they represent a certain proportion of the total images.
Referring back to the quantitative findings of Chapter 5, it is evident that
the ratio of images in a particular subclass is decisive for the accuracy of
the results, regardless of whether it is gender, racial, or age diversity.
This argument is also underlined by the research from zhu2018 who studied the
effects of racially balanced datasets and highlighted the importance of
maintaining a balance between images depicting black and white, male and
female individuals. Hence, in order to cope with black and white/sepia
filters, independent datasets must be developed.

Figure 14: Interview participants results and suggestions.

Another issue raised by the participants A1, A2 and B is the blindfolding and
covering of victim’s faces. This enables child abusers to conceal the identity
of the victims and possibly stay undetected for a longer period of time
because different cases cannot be related to one another based on the child’s
face. Even unintentional disguises of the face such as blurs and bad image
quality, in general, are causing problems for investigators as well as for age
estimation software. In order to overcome the problem of partial or full
facial concealments, age assessment needs to progress in a different
direction. Besides the face of an individual, many other features of the human
body are carrying age information. Few studies, including machado2019, are
exploring age estimation from facial proportions, which still could be
effective if victims are blindfolded or wearing masks. However, alternative
methods, such as cranial and dental age estimation as well as the age
estimation based on the sexual development stages are difficult if not
impossible to carry out by only having image and video material available. As
presented in Figure 14, participant B points out similar issues in digital
forensic investigations: Besides the massive backlog and racial bias, blurry
images (intentional and unintentional face blurs), as well as full/partial
face occlusions are causing problems in age estimation. Furthermore, the
thematic analysis of interview B and C revealed that there is a need for
better mental health counselling. In contrast, participant A1 and A2
personally pointed out that they have always received adequate mental health
counselling and had various other opportunities to cope with work-related
psychological stress. In general, however, the data found in seigfried2018
suggests that the majority of individuals performing duties related to CSAM
are not adequately coping with work-related stress, based on a sample of 129
law enforcement officers. carleton2018 support this view, who discovered that
mental disorder symptoms are more prevalent within public safety personnel
than previously expected. Hence, since human investigators will remain a vital
part of digital forensic investigations for the next decades, much more
attention needs to be paid to mental health counselling. Participant C
addressed the questions from a more generic point of view by highlighting the
most prevalent challenges in the digital forensics domain – the encryption and
obfuscation of CSAM (Figure 14). This view is underlined by the findings of
the Literature Review on the massive growth of cloud forensics as well as the
increase in encrypted image and video material. Evidence collection and
recovery is challenging within a cloud environment, not only because of the
sheer amount of data but also due to jurisdiction problems that can occur
[pichan2015]. From a non-technical point of view, the interview with
participant C revealed that the age of consent boundary needs to be more in
the focus of researchers. Although the emphasis on the exact age estimation of
a victim remains important, the binary categorisation into adult or non-adult
should be the main target. This research already addresses this problem with
the embedded age estimation functionality.

###  6.2 Limitations and Future Work

The confirmability and transferability of qualitative research could be
further enhanced by testing the prototype efficacy more broadly. Currently,
this research is solely aimed at studying digital forensic age estimation in
the UK. However, processes vary significantly across different countries and
legislations. To get a more profound picture of the potential efficacy of the
research, the age estimation prototype needs to be tested in cooperation with
multiple digital forensic departments across different countries. Hence,
future research could overcome this problem by extending the qualitative
interviews and software evaluation to a survey with a larger sample size. In
future work, the testing dataset should be enlarged to get more accurate
insights into the underlying dynamics of various pre-trained age estimation
models. New datasets can be collected that particularly focus on black & white
images as well as partial facial concealments. Unfortunately, since many well-
suited facial image datasets remain private, they cannot be merged with the
new dataset. The increase in demand for publicly-shared datasets is not only
limited to this domain but can be observed across many other scientific
fields. In order to maximise the knowledge gain by researchers, recent studies
[patel2017, shrestha2018] are reviewing options to apply blockchain technology
to the secure distribution of scientific datasets. Furthermore, there is
remaining opportunity for further research in the area of coalitions against
CSAE. A promising approach is presented by The Technology Coalition
(techcoaltion2020). This organisation brings together different companies
across the technology industry, such as Microsoft, Google and Facebook, and
relies on sharing best practices and coordinating efforts to combat CSAE. In
the last decade, these companies have developed and consistently supported the
research in the area of machine learning. By leveraging their seemingly
unlimited access to knowledge, computing power and other resources, big
coalitions are the most promising way to combat CSAE. Hence, researchers in
the area of age estimation should be on the lookout for ways to integrate
their work and thus contribute to existing coalitions in order to create more
accurate solutions.

###  6.3 Conclusion

The aim of this research is to enhance digital forensic investigations by
assessing current age estimation solutions, reviewing facial image datasets
and developing an age estimation prototype. The results show that even very
large publicly available facial image datasets and age estimation models can
be unsuitable for digital forensic investigations due to factors such as image
scarcity in lower age ranges and low image diversity. By categorising,
evaluating and critiquing the age estimation challenges as well as the
surrounding issues in the domain, this research shows that digital forensic
investigations can benefit from age estimation software by reducing the mental
stress on investigators and potential cognitive bias. Based on the
quantitative analysis of the age estimation results, it can be concluded that
estimation results around the age of consent boundary show comparable
performance to state-of-the-art solutions. Although the age estimation
accuracy of young children is imprecise, it does not greatly affect DFIs since
the approximate age of young children can generally be correctly determined by
investigators. The qualitative interview and software evaluation support this
research with qualitative insights from digital forensic experts. They aid the
presentation of age estimation results with age probability graphs and the
accuracy evaluation using the CS. The findings also reveal that investigators
are concerned about racial bias, blurry images as well as full/partial face
occlusions. Returning to the original problem statement, this research further
progresses digital forensic investigations towards fully-automated age
estimation processes by showing the immense potential of CNNs and highlighting
the limitations of existing solutions. Research that is built upon this thesis
can enhance workflows in DFIs, reduce the psychological burden on
investigators and thus ultimately combat CSAE-related cybercrime.