4 research outputs found
Reducing Geographic Performance Differential for Face Recognition
As face recognition algorithms become more accurate and get deployed more
widely, it becomes increasingly important to ensure that the algorithms work
equally well for everyone. We study the geographic performance
differentials-differences in false acceptance and false rejection rates across
different countries-when comparing selfies against photos from ID documents. We
show how to mitigate geographic performance differentials using sampling
strategies despite large imbalances in the dataset. Using vanilla domain
adaptation strategies to fine-tune a face recognition CNN on domain-specific
doc-selfie data improves the performance of the model on such data, but, in the
presence of imbalanced training data, also significantly increases the
demographic bias. We then show how to mitigate this effect by employing
sampling strategies to balance the training procedure.Comment: Demographic Variation in the Performance of Biometric Systems
workshop at WACV 202
Identity Document to Selfie Face Matching Across Adolescence
Matching live images (``selfies'') to images from ID documents is a problem
that can arise in various applications. A challenging instance of the problem
arises when the face image on the ID document is from early adolescence and the
live image is from later adolescence. We explore this problem using a private
dataset called Chilean Young Adult (CHIYA) dataset, where we match live face
images taken at age 18-19 to face images on ID documents created at ages 9 to
18. State-of-the-art deep learning face matchers (e.g., ArcFace) have
relatively poor accuracy for document-to-selfie face matching. To achieve
higher accuracy, we fine-tune the best available open-source model with triplet
loss for a few-shot learning. Experiments show that our approach achieves
higher accuracy than the DocFace+ model recently developed for this problem.
Our fine-tuned model was able to improve the true acceptance rate for the most
difficult (largest age span) subset from 62.92% to 96.67% at a false acceptance
rate of 0.01%. Our fine-tuned model is available for use by other researchers
Improving Presentation Attack Detection for ID Cards on Remote Verification Systems
In this paper, an updated two-stage, end-to-end Presentation Attack Detection
method for remote biometric verification systems of ID cards, based on
MobileNetV2, is presented. Several presentation attack species such as printed,
display, composite (based on cropped and spliced areas), plastic (PVC), and
synthetic ID card images using different capture sources are used. This
proposal was developed using a database consisting of 190.000 real case Chilean
ID card images with the support of a third-party company. Also, a new framework
called PyPAD, used to estimate multi-class metrics compliant with the ISO/IEC
30107-3 standard was developed, and will be made available for research
purposes. Our method is trained on two convolutional neural networks
separately, reaching BPCER\textsubscript{100} scores on ID cards attacks of
1.69\% and 2.36\% respectively. The two-stage method using both models together
can reach a BPCER\textsubscript{100} score of 0.92\%
Learning Meta Face Recognition in Unseen Domains
Face recognition systems are usually faced with unseen domains in real-world
applications and show unsatisfactory performance due to their poor
generalization. For example, a well-trained model on webface data cannot deal
with the ID vs. Spot task in surveillance scenario. In this paper, we aim to
learn a generalized model that can directly handle new unseen domains without
any model updating. To this end, we propose a novel face recognition method via
meta-learning named Meta Face Recognition (MFR). MFR synthesizes the
source/target domain shift with a meta-optimization objective, which requires
the model to learn effective representations not only on synthesized source
domains but also on synthesized target domains. Specifically, we build
domain-shift batches through a domain-level sampling strategy and get
back-propagated gradients/meta-gradients on synthesized source/target domains
by optimizing multi-domain distributions. The gradients and meta-gradients are
further combined to update the model to improve generalization. Besides, we
propose two benchmarks for generalized face recognition evaluation. Experiments
on our benchmarks validate the generalization of our method compared to several
baselines and other state-of-the-arts. The proposed benchmarks will be
available at https://github.com/cleardusk/MFR.Comment: Accepted to CVPR2020 (Oral