19 research outputs found
Rethinking the Domain Gap in Near-infrared Face Recognition
Heterogeneous face recognition (HFR) involves the intricate task of matching
face images across the visual domains of visible (VIS) and near-infrared (NIR).
While much of the existing literature on HFR identifies the domain gap as a
primary challenge and directs efforts towards bridging it at either the input
or feature level, our work deviates from this trend. We observe that large
neural networks, unlike their smaller counterparts, when pre-trained on large
scale homogeneous VIS data, demonstrate exceptional zero-shot performance in
HFR, suggesting that the domain gap might be less pronounced than previously
believed. By approaching the HFR problem as one of low-data fine-tuning, we
introduce a straightforward framework: comprehensive pre-training, succeeded by
a regularized fine-tuning strategy, that matches or surpasses the current
state-of-the-art on four publicly available benchmarks. Corresponding codes can
be found at https://github.com/michaeltrs/RethinkNIRVIS.Comment: 5 pages, 3 figures, 6 table
Grouped Knowledge Distillation for Deep Face Recognition
Compared with the feature-based distillation methods, logits distillation can
liberalize the requirements of consistent feature dimension between teacher and
student networks, while the performance is deemed inferior in face recognition.
One major challenge is that the light-weight student network has difficulty
fitting the target logits due to its low model capacity, which is attributed to
the significant number of identities in face recognition. Therefore, we seek to
probe the target logits to extract the primary knowledge related to face
identity, and discard the others, to make the distillation more achievable for
the student network. Specifically, there is a tail group with near-zero values
in the prediction, containing minor knowledge for distillation. To provide a
clear perspective of its impact, we first partition the logits into two groups,
i.e., Primary Group and Secondary Group, according to the cumulative
probability of the softened prediction. Then, we reorganize the Knowledge
Distillation (KD) loss of grouped logits into three parts, i.e., Primary-KD,
Secondary-KD, and Binary-KD. Primary-KD refers to distilling the primary
knowledge from the teacher, Secondary-KD aims to refine minor knowledge but
increases the difficulty of distillation, and Binary-KD ensures the consistency
of knowledge distribution between teacher and student. We experimentally found
that (1) Primary-KD and Binary-KD are indispensable for KD, and (2)
Secondary-KD is the culprit restricting KD at the bottleneck. Therefore, we
propose a Grouped Knowledge Distillation (GKD) that retains the Primary-KD and
Binary-KD but omits Secondary-KD in the ultimate KD loss calculation. Extensive
experimental results on popular face recognition benchmarks demonstrate the
superiority of proposed GKD over state-of-the-art methods.Comment: 9 pages, 2 figures, 7 tables, accepted by AAAI 202
EFaR 2023: Efficient Face Recognition Competition
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 received 17 submissions from 6 different teams. To drive
further development of efficient face recognition models, the submitted
solutions are ranked based on a weighted score of the achieved verification
accuracies on a diverse set of benchmarks, as well as the deployability given
by the number of floating-point operations and model size. The evaluation of
submissions is extended to bias, cross-quality, and large-scale recognition
benchmarks. Overall, the paper gives an overview of the achieved performance
values of the submitted solutions as well as a diverse set of baselines. The
submitted solutions use small, efficient network architectures to reduce the
computational cost, some solutions apply model quantization. An outlook on
possible techniques that are underrepresented in current solutions is given as
well.Comment: Accepted at IJCB 202