158 research outputs found
SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic Data
Recently, significant progress has been made in face presentation attack
detection (PAD), which aims to secure face recognition systems against
presentation attacks, owing to the availability of several face PAD datasets.
However, all available datasets are based on privacy and legally-sensitive
authentic biometric data with a limited number of subjects. To target these
legal and technical challenges, this work presents the first synthetic-based
face PAD dataset, named SynthASpoof, as a large-scale PAD development dataset.
The bona fide samples in SynthASpoof are synthetically generated and the attack
samples are collected by presenting such synthetic data to capture systems in a
real attack scenario. The experimental results demonstrate the feasibility of
using SynthASpoof for the development of face PAD. Moreover, we boost the
performance of such a solution by incorporating the domain generalization tool
MixStyle into the PAD solutions. Additionally, we showed the viability of using
synthetic data as a supplement to enrich the diversity of limited authentic
training data and consistently enhance PAD performances. The SynthASpoof
dataset, containing 25,000 bona fide and 78,800 attack samples, the
implementation, and the pre-trained weights are made publicly available.Comment: Accepted at CVPR workshop 202
Optimization-Based Improvement of Face Image Quality Assessment Techniques
Contemporary face recognition (FR) models achieve near-ideal recognition
performance in constrained settings, yet do not fully translate the performance
to unconstrained (realworld) scenarios. To help improve the performance and
stability of FR systems in such unconstrained settings, face image quality
assessment (FIQA) techniques try to infer sample-quality information from the
input face images that can aid with the recognition process. While existing
FIQA techniques are able to efficiently capture the differences between high
and low quality images, they typically cannot fully distinguish between images
of similar quality, leading to lower performance in many scenarios. To address
this issue, we present in this paper a supervised quality-label optimization
approach, aimed at improving the performance of existing FIQA techniques. The
developed optimization procedure infuses additional information (computed with
a selected FR model) into the initial quality scores generated with a given
FIQA technique to produce better estimates of the "actual" image quality. We
evaluate the proposed approach in comprehensive experiments with six
state-of-the-art FIQA approaches (CR-FIQA, FaceQAN, SER-FIQ, PCNet, MagFace,
SDD-FIQA) on five commonly used benchmarks (LFW, CFPFP, CPLFW, CALFW, XQLFW)
using three targeted FR models (ArcFace, ElasticFace, CurricularFace) with
highly encouraging results.Comment: In proceedings of the International Workshop on Biometrics and
Forensics (IWBF) 202
Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection
Face recognition (FR) systems continue to spread in our daily lives with an
increasing demand for higher explainability and interpretability of FR systems
that are mainly based on deep learning. While bias across demographic groups in
FR systems has already been studied, the bias of explainability tools has not
yet been investigated. As such tools aim at steering further development and
enabling a better understanding of computer vision problems, the possible
existence of bias in their outcome can lead to a chain of biased decisions. In
this paper, we explore the existence of bias in the outcome of explainability
tools by investigating the use case of face presentation attack detection. By
utilizing two different explainability tools on models with different levels of
bias, we investigate the bias in the outcome of such tools. Our study shows
that these tools show clear signs of gender bias in the quality of their
explanations
Demographic Bias in Presentation Attack Detection of Iris Recognition Systems
With the widespread use of biometric systems, the demographic bias problem
raises more attention. Although many studies addressed bias issues in biometric
verification, there are no works that analyze the bias in presentation attack
detection (PAD) decisions. Hence, we investigate and analyze the demographic
bias in iris PAD algorithms in this paper. To enable a clear discussion, we
adapt the notions of differential performance and differential outcome to the
PAD problem. We study the bias in iris PAD using three baselines (hand-crafted,
transfer-learning, and training from scratch) using the NDCLD-2013 database.
The experimental results point out that female users will be significantly less
protected by the PAD, in comparison to males.Comment: accepted for publication at EUSIPCO202
Synthetic Data for Face Recognition: Current State and Future Prospects
Over the past years, deep learning capabilities and the availability of
large-scale training datasets advanced rapidly, leading to breakthroughs in
face recognition accuracy. However, these technologies are foreseen to face a
major challenge in the next years due to the legal and ethical concerns about
using authentic biometric data in AI model training and evaluation along with
increasingly utilizing data-hungry state-of-the-art deep learning models. With
the recent advances in deep generative models and their success in generating
realistic and high-resolution synthetic image data, privacy-friendly synthetic
data has been recently proposed as an alternative to privacy-sensitive
authentic data to overcome the challenges of using authentic data in face
recognition development. This work aims at providing a clear and structured
picture of the use-cases taxonomy of synthetic face data in face recognition
along with the recent emerging advances of face recognition models developed on
the bases of synthetic data. We also discuss the challenges facing the use of
synthetic data in face recognition development and several future prospects of
synthetic data in the domain of face recognition.Comment: Accepted at Image and Vision Computing 2023 (IVC 2023
Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation
Explainable Face Recognition is gaining growing attention as the use of the
technology is gaining ground in security-critical applications. Understanding
why two faces images are matched or not matched by a given face recognition
system is important to operators, users, anddevelopers to increase trust,
accountability, develop better systems, and highlight unfair behavior. In this
work, we propose xSSAB, an approach to back-propagate similarity score-based
arguments that support or oppose the face matching decision to visualize
spatial maps that indicate similar and dissimilar areas as interpreted by the
underlying FR model. Furthermore, we present Patch-LFW, a new explainable face
verification benchmark that enables along with a novel evaluation protocol, the
first quantitative evaluation of the validity of similarity and dissimilarity
maps in explainable face recognition approaches. We compare our efficient
approach to state-of-the-art approaches demonstrating a superior trade-off
between efficiency and performance. The code as well as the proposed Patch-LFW
is publicly available at: https://github.com/marcohuber/xSSAB
ExFaceGAN: Exploring Identity Directions in GAN's Learned Latent Space for Synthetic Identity Generation
Deep generative models have recently presented impressive results in
generating realistic face images of random synthetic identities. To generate
multiple samples of a certain synthetic identity, several previous works
proposed to disentangle the latent space of GANs by incorporating additional
supervision or regularization, enabling the manipulation of certain attributes,
e.g. identity, hairstyle, pose, or expression. Most of these works require
designing special loss functions and training dedicated network architectures.
Others proposed to disentangle specific factors in unconditional pretrained
GANs latent spaces to control their output, which also requires supervision by
attribute classifiers. Moreover, these attributes are entangled in GAN's latent
space, making it difficult to manipulate them without affecting the identity
information. We propose in this work a framework, ExFaceGAN, to disentangle
identity information in state-of-the-art pretrained GANs latent spaces,
enabling the generation of multiple samples of any synthetic identity. The
variations in our generated images are not limited to specific attributes as
ExFaceGAN explicitly aims at disentangling identity information, while other
visual attributes are randomly drawn from a learned GAN latent space. As an
example of the practical benefit of our ExFaceGAN, we empirically prove that
data generated by ExFaceGAN can be successfully used to train face recognition
models.Comment: Accepted at IJCB 202
Beyond Identity: What Information Is Stored in Biometric Face Templates?
Deeply-learned face representations enable the success of current face
recognition systems. Despite the ability of these representations to encode the
identity of an individual, recent works have shown that more information is
stored within, such as demographics, image characteristics, and social traits.
This threatens the user's privacy, since for many applications these templates
are expected to be solely used for recognition purposes. Knowing the encoded
information in face templates helps to develop bias-mitigating and
privacy-preserving face recognition technologies. This work aims to support the
development of these two branches by analysing face templates regarding 113
attributes. Experiments were conducted on two publicly available face
embeddings. For evaluating the predictability of the attributes, we trained a
massive attribute classifier that is additionally able to accurately state its
prediction confidence. This allows us to make more sophisticated statements
about the attribute predictability. The results demonstrate that up to 74
attributes can be accurately predicted from face templates. Especially
non-permanent attributes, such as age, hairstyles, haircolors, beards, and
various accessories, found to be easily-predictable. Since face recognition
systems aim to be robust against these variations, future research might build
on this work to develop more understandable privacy preserving solutions and
build robust and fair face templates.Comment: To appear in IJCB 202
Fairness in Face Presentation Attack Detection
Face presentation attack detection (PAD) is critical to secure face
recognition (FR) applications from presentation attacks. FR performance has
been shown to be unfair to certain demographic and non-demographic groups.
However, the fairness of face PAD is an understudied issue, mainly due to the
lack of appropriately annotated data. To address this issue, this work first
presents a Combined Attribute Annotated PAD Dataset (CAAD-PAD) by combining
several well-known PAD datasets where we provide seven human-annotated
attribute labels. This work then comprehensively analyses the fairness of a set
of face PADs and its relation to the nature of training data and the
Operational Decision Threshold Assignment (ODTA) on different data groups by
studying four face PAD approaches on our CAAD-PAD. To simultaneously represent
both the PAD fairness and the absolute PAD performance, we introduce a novel
metric, namely the Accuracy Balanced Fairness (ABF). Extensive experiments on
CAAD-PAD show that the training data and ODTA induce unfairness on gender,
occlusion, and other attribute groups. Based on these analyses, we propose a
data augmentation method, FairSWAP, which aims to disrupt the identity/semantic
information and guide models to mine attack cues rather than attribute-related
information. Detailed experimental results demonstrate that FairSWAP generally
enhances both the PAD performance and the fairness of face PAD
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