# Supervised Contrastive Learning for Snapshot Spectral Imaging Face Anti-
Spoofing

Chuanbiao Song1, Yan Hong1, Jun Lan1,
Huijia Zhu1, Weiqiang Wang1, Jianfu Zhang2
1Ant Group, 2Qing Yuan Research Institute, Shanghai Jiao Tong University
<EMAIL_ADDRESS><EMAIL_ADDRESS>
1{yelan.lj, huijia.zhj<EMAIL_ADDRESS><EMAIL_ADDRESS>Corresponding author.

###### Abstract

This study reveals a cutting-edge re-balanced contrastive learning strategy
aimed at strengthening face anti-spoofing capabilities within facial
recognition systems, with a focus on countering the challenges posed by
printed photos, and highly realistic silicone or latex masks. Leveraging the
HySpeFAS dataset, which benefits from Snapshot Spectral Imaging technology to
provide hyperspectral images, our approach harmonizes class-level contrastive
learning with data resampling and an innovative real-face oriented reweighting
technique. This method effectively mitigates dataset imbalances and reduces
identity-related biases. Notably, our strategy achieved an unprecedented
0.0000% Average Classification Error Rate (ACER) on the HySpeFAS dataset,
ranking first at the Chalearn Snapshot Spectral Imaging Face Anti-spoofing
Challenge on CVPR 2024.

##  1 Introduction

Face recognition technologies [15, 19, 14, 9, 2, 1, 22], with the extensive
applications in various aspects of our life like mobile payments and access
control systems, has significantly enhanced convenience. Nonetheless, its
susceptibility to diverse forms of attacks limits its reliable application.
Numerous malicious attacks, including the use of printed photos, video
replays, and faces with flexible masks, can readily mislead these systems into
making wrong judgments. To ensure the dependable operation of face recognition
systems, face anti-spoofing (FAS) [25, 23, 25, 10] methods are crucial for
identifying and mitigating various attacks.

Confronted with the challenge of highly convincing silicone or latex masks,
the deployment of innovative spectroscopy sensors [20, 17] can notably boost
the discriminative power of face recognition systems against these attacks.
Snapshot Spectral Imaging (SSI) [3, 8, 7] technologies possess the ability to
capture compressed sensing spectral images, positioning it as an effective
tool for the integration of spectroscopic information into current face
recognition systems. Recently, utilizing a snapshot spectral camera, the
Chalearn Snapshot Spectral Imaging Face Anti-spoofing Challenge at CVPR 2024
successfully acquires SSI images of both real and fake faces, and creates the
first snapshot spectral face anti-spoofing dataset, named HySpeFAS. This
dataset encompasses 6760 hyperspectral images, each reconstructed from SSI
images using the TwIST [18] algorithm and featuring 30 spectral channels.
These data present invaluable opportunities for FAS to advance the
sophistication and reliability of algorithms.

In this paper, we present our approach tailored for the FAS task. We design a
re-balanced contrastive learning approach, aimed at capturing the detailed and
intrinsic patterns from the imbalanced dataset. We embed class-level
contrastive learning into FAS task by employing data resampling to mitigate
class-level imbalances in the dataset. Furthermore, we introduce an innovative
real face-oriented reweighting methodology to effectively eliminate potential
bias to the identity of the face. The proposed method achieves 0.0000% ACER on
the HySpeFAS dataset and ranks first place on the Snapshot Spectral Imaging
Face Anti-spoofing Challenge at CVPR 2024.

Figure 1: Analysis of examples from HySpeFAS dataset. From top to bottom: real
images and fake images. From left to right: examples in the first box show the
same ID face across real and fake images; the second one indicates the various
alternations of real and fake images; the third one visualizes different
appearances of images from the same ID; the last one shows different
orientations of real images and fake images.

Data |  Total |  Fake Class |  Real Class
---|---|---|---
Train |  3900 |  3380 |  520
Val |  936 |  728 |  208
Test |  1924 |  - |  -

Table 1: The split of train/validation/test real images and fake images on the
HySpeFAS dataset.

##  2 HySpeFAS dataset

The HySpeFAS dataset utilizes a snapshot spectral camera to obtain SSI images
of both real and fake faces, and those images are reconstructed from SSI
images by TwIST algorithm and characterized with 30 spectral channels. In
total, the dataset provides 6760 hyperspectral images, as detailed in Table 1.
For the Snapshot Spectral Imaging Face Anti-spoofing Challenge at CVPR 2024,
the organizers have provided all images along with their spectral matrices.
The dataset is divided into a training set with 3900 images and a validation
set containing 936 images. Visual examples from the dataset are shown in
Figure 1, facilitating an in-depth analysis of the HySpeFAS dataset. This
analysis serves as the foundation for data resampling and reweighting
strategies discussed in Section 3.

From Table 1, it is evident that although the dataset is valuable due to the
challenging acquisition process, the quantity of data remains limited.
Additionally, the number of counterfeit face images significantly outnumbers
that of genuine faces, creating a pronounced imbalance between the two primary
classes of images. In Figure 1, several key characteristics of the HySpeFAS
dataset are identified: (1). identical identifiers (ID) are present in both
fake and real face images. (2). face images exhibit a variety of alterations,
including masks and transparent masks. (3). faces from the same ID show
considerable variation in physical appearance. (4). variations in facial
orientation and other conditions are also observed. These characteristics
present considerable challenges, and the methodology proposed in this paper is
designed to address these specific aspects.

Figure 2: The framework of our proposed method.

##  3 Methodology

In this section, we first present the data preprocessing for the HySpeFAS
dataset on the basis of data analysis in Section 2. Then we introduce our
framework comprised of multiple modules. Next, we describe the used loss
function compatible with different modules. Finally, the intra-class mixup
[26], the real face-oriented reweighting, and the cross batch memory [21] are
integrated into the training strategy to promote the supervised contrastive
learning.

###  3.1 Data Preprocessing

Given the volume and the imbalance between the real and fake sample quantities
within the dataset, we initiate our approach with preprocessing enhancements
to address this issue.

##### Class Balancing

As shown in Table 1, we analyze the class numbers of the training data and
validation data, and can observe that the HySpeFAS dataset is an unbalanced
dataset, where the number of fake data is much larger than that of real data.
To eliminate the effects of the imbalanced data, we adopt the oversampling
strategy to rebalance the data distribution by amplifying the volume of real
instances.

##### Data Augmentations

During the training, we use extensive data augmentations, such as random crop,
random horizontal flip, cutout [5] and random mask. For random mask, we
randomly mask the bottom half of training samples to eliminate the effect of
the worn mask, or randomly mask the left or right half of training samples.
Figure 3 presents some examples of augmented faces based on random mask.

Figure 3: Visualization of augmented examples with three different type of
random mask.

###  3.2 Framework

Our framework, shown in Figure 2, leverage the multi-attention network MAT
[27] as the backbone $\mathbf{E}_{mat}$, and combine the spectral weight
learning module [6] $\mathbf{E}_{swl}$, the central differential convolution
[24] $\mathbf{E}_{cdc}$, the classifier $\mathbf{E}_{c}$, and the contrastive
learning module $\mathbf{E}_{scl}$. image-label pairs from HySpeFAS dataset
can be represented as $\\{\bm{x}_{I},\bm{x}_{m},\bm{y}\\}$, where
$\bm{x}_{I}\in\mathbb{R}^{w\times h\times 3}$ (_resp.,_
$\bm{x}_{m}\in\mathbb{R}^{w\times h\times 30}$, and $\bm{y}\in\mathbb{R}^{2}$)
denotes RGB image (_resp.,_ spectral matrix, and one-hot label). We
concatenate $\bm{x}_{I}$ and $\bm{x}_{m}$ along the third channel to form
input sample $\bm{x}\in\mathbb{R}^{w\times h\times 33}$.

Given the multimodal nature of the input, input sample $\bm{x}$ first is
processed with the spectral weight learning module $\mathbf{E}_{swl}$ to
assign different weights to input channels. Then we adopt the
$\mathbf{E}_{cdc}$ as the first convolution layer to obtain gradient-level
features, and use a multi-attentional network $\mathbf{E}_{mat}$ to learn
discriminative features. Feded with the learned discriminative features, the
classifier $\mathbf{E}_{c}$ is responsible for distinguishing fake faces from
real faces, while the contrastive learning module $\mathbf{E}_{scl}$ is
dedicated to promoting the discriminability of the learned features.

###  3.3 Training Strategies

####  3.3.1 Intra-class Mixup

Given the limited size of the training dataset, the risk of overfitting is
heightened when employing large neural networks. To mitigate this and enrich
the diversity of training samples, we utilize a variant of mixup, inter-class
mixup. As formulated by Eqn. 1, inter-class mixup generates training samples
$(\hat{\bm{x}},\hat{y})$ by interpolating between two training samples from
the same class.

$\begin{gathered}\hat{\bm{x}}=\lambda\cdot{\bm{x}_{i}}+(1-\lambda)\cdot\bm{x}_{j},\\\
\hat{\bm{y}}=\lambda\cdot\bm{y}_{i}+(1-\lambda)\cdot\bm{y}_{j},\\\
\end{gathered}$ (1)

where $\bm{y}_{i}$ and $\bm{y}_{j}$ are the one-hot labels of the same class,
and $\lambda\in[0,1]$ represents the mixing parameter. Following the setting
of the original mixup, we set $\lambda\sim\mathrm{Beta(1.0,1.0)}$.

####  3.3.2 Real-face Oriented Reweighting

To diminish the model’s reliance on content irrelevant to spoofing detection,
such as identity and facial features, we introduce Real-face Oriented
Reweighting(ROR) strategy during training. ROR assigns weights to fake
training samples based on their face cosine similarity with real training
samples. The face cosine similarities are calculated based on the typical face
model ArcFace [4] as follows,

$\begin{gathered}w_{\bm{x}^{F}_{i}}=\max\frac{1+cos(f_{\mathrm{Arcface}}(\bm{x}^{F}_{i}),f_{\mathrm{Arcface}}(\bm{x}^{R}_{j}))}{2},\\\
\end{gathered}$ (2)

where $\bm{x}^{R}_{i}$ (_resp._ , $\bm{x}^{F}_{j}$) denotes the real face
image (_resp._ ,fake face images). Fake samples exhibiting higher similarity
to real faces are assigned greater weights. This reweighting approach
encourages the model to deprioritize learning from features strongly tied to
identity and facial appearance, thereby focusing more on distinguishing
genuine from faces.

####  3.3.3 Objective Functions

We design two distinct loss functions: one loss for classification, and a
contrastive loss for regularizing real and fake data features. These losses
are combined in a weighted sum to create the overall loss function for
training the framework.

##### Focal Loss.

Instead of using the typical cross entropy for classification, we adopt the
focal loss [12], which is based on a variant of cross entropy for binary
classification:

$\mathcal{L}_{\text{c}}=\mathrm{FL}(p_{t})=-(1-p_{t})^{\gamma}log(p_{t}),$ (3)

where $p_{t}$ is the probability that the model predicts for the ground truth
object and we set $\gamma$ as 2. According to Eqn. 3, the focal loss gives
less weight to easy examples and gives more weight to hard misclassified
examples.

##### Supervised Contrastive Loss.

The objective of contrastive regularization loss is to optimize the similarity
and dissimilarity of real and fake data embeddings. The contrastive regulation
loss is formulated as:

$\begin{gathered}\mathcal{L}_{i}^{\text{sup }}=\sum_{j=1}^{N}1_{i\neq j}\cdot
1_{\tilde{y}_{i}=\tilde{y}_{j}}\cdot\log\frac{\exp\left(\bm{z}_{i}\cdot\bm{z}_{j}/\tau\right)}{\sum_{k=1}^{N}1_{i\neq
k}\cdot\exp\left(\bm{z}_{i}\cdot\bm{z}_{k}/\tau\right)},\\\
\mathcal{L}_{\text{scl }}=-\sum_{i=1}^{N}\mathcal{L}_{i}^{\text{sup
}},\end{gathered}$ (4)

where $\bm{z}_{i}$ is denoted as the normalized embedding of the training
sample $\bm{x}_{i}$ from $\mathbf{E}_{scl}$ module, and $\tau$ serves as a
temperature hyper-parameter.

According to Eqn. 4, the supervised contrastive loss maximizes the cosine
similarity between the training samples with the same category, while
simultaneously minimizing the cosine similarity between the training samples
with different categories. We compute the loss between real and fake samples
to encourage the model to learn a generalizable representation that across
different images.

To boost the performance of the supervised contrastive loss, we utilize the
cross batch memory (XBM) [21] to collect sufficient hard negative pairs for
contrastive learning. Specifically, XBM memorizes the embeddings of recent
mini-batches and can provide sufficient embeddings for calculating the
supervised contrastive loss. It operates on a queue principle, enrolling the
latest batch of embeddings while simultaneously removing the oldest,
maintaining a dynamic and up-to-date memory bank for optimization.

Overall Loss. The final loss function of the training process is the weighted
sum of the above loss functions:

$\mathcal{L}=\mathcal{L}_{\text{c}}+\lambda_{scl}\cdot\mathcal{L}_{\text{scl}},$
(5)

where $\lambda_{scl}$ is a hyper-parameter for balancing the overall loss, and
we set $\lambda_{scl}$ as 10 for a strong regularization.

##  4 Experiments

In this section, we will describe the evaluation metrics, training details, as
well as the performance of our proposed method on the HySpeFAS dataset.

###  4.1 Evaluation Metrics

Following the HySpeFAS dataset, we select the Attack Presentation
Classification Error Rate (APCER), Bona Fide Presentation Classification Error
Rate (BPCER), and Average Classification Error Rate (ACER) as the evaluation
metric. Specifically, APCER and BPCER are formulated as below:

$APCER=\frac{FN}{TP+FN},\ \ BPCER=\frac{FP}{FP+TN},$ (6)

where FN(False Negative) and FP(False Positive) refer to the number of
incorrectly classified fake or real samples respectively, and TP(True
Positive) and TN(True Negative) refer to the number of correctly classified
real or fake samples respectively. The ACER is calculated as below:

$ACER=\frac{APCER+BPCER}{2},$ (7)

which is used as the main evaluation metric on the test set and determine the
final ranking of the competition. The lower the ACER value, the better the
performance.

###  4.2 Training Details

We implement our method on 1 NVIDIA Tesla A100 80G GPU based on open-source
framework PyTorch [16]. We train the network using the ASAM optimizer [11]
with 30 epochs. The learning rate is 0.01 initially and adjusted by the cosine
annealing schedule [13]. The batch size is 240 and the weight decay is
$5\times 10^{-3}$, and the memory size of the XBM [21] is 1200. The
temperature parameter $\tau$ of the supervised contrastive loss is set to
0.07.

###  4.3 Performance Results

We compare the performance of our method and the solutions of other teams on
the test set in the Snapshot Spectral Imaging Face Anti-spoofing Challenge at
CVPR 2024. The evaluation scores of ours and other teams are shown in Table 2.
We can observe that all the top 10 teams achieve excellent performance
results, where all the ACER results are less than 1%. Our method achieves
ACER, APCER, and BPCER by 0%, 0% and 0%, respectively, ranking the first place
in this competition.

Team | ACER(%) | APCER(%) | BPCER(%)
---|---|---|---
DXAI | 0.7237 | 0.8065 | 0.6410
ZTT | 0.6927 | 0.7444 | 0.6410
galileo | 0.6359 | 0.3102 | 0.9615
kk_li | 0.6359 | 0.3102 | 0.9615
ctyun-ai | 0.4756 | 0.3102 | 0.6410
Ricardozzf | 0.2223 | 0.1241 | 0.3205
hexianhua | 0.1861 | 0.3722 | 0.0000
SeaRecluse | 0.0310 | 0.0620 | 0.0000
CTEL_AI | 0.0000 | 0.0000 | 0.0000
Ours | 0.0000 | 0.0000 | 0.0000

Table 2: The top10 leaderboard of the Snapshot Spectral Imaging Face Anti-
spoofing Challenge at CVPR 2024.

##  5 Conclusion

In this paper, we introduce supervised contrastive learning for snapshot
spectral imaging face anti-spoofing based on the multi-attention neural
network. Furthermore, to boost the supervised contrastive learning, we utilize
the intra-class mixup to improve the diversity of training samples, the real-
face oriented sample reweighting to avoid the effects of the identity feature,
and the cross-batch memory to increase the number of the contrastive samples.
Experimental results show that the proposed method achieves excellent
performance and yields the first place among all teams on the recently
conducted the Snapshot Spectral Imaging Face Anti-spoofing Challenge at CVPR
2024.

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