The development of deep learning algorithms has extensively empowered humanity's task
automatization capacity. However, the huge improvement in the performance of these …

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held
at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition …

Deep generative models have recently presented impressive results in generating realistic
face images of random synthetic identities. To generate multiple samples of a certain …

Current periocular and face recognition approaches utilize computationally costly deep
neural networks, achieving notable recognition accuracies. Deploying such solutions in …

Liveness Detection (LivDet) is an international competition series open to academia and
industry with the objective to assess and report state-of-the-art in Presentation Attack …

Periocular Biometrics: A Modality for Unconstrained Scenarios Page 1 COVER FEATURE
BIOMETRICS, BETTER LONGEVITY, AND HEALTH 40 COMPUTER PUBLISHED BY THE IEEE …

State-of-the-art face recognition (FR) systems are based on overparameterized deep neural
networks (DNN) which commonly use face images with 256 3 colors. The use of DNN and …

Most network pruning methods focus on identifying redundant channels from pre-trained
models, which is inefficient due to its three-step process: pre-training, pruning and fine …

This paper presents a summary of the Competition on Face Presentation Attack Detection
Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 …

As in school, one teacher to cover all subjects is insufficient to distill equally robust
information to a student. Hence, each subject is taught by a highly specialised teacher …