SEEKr: Efficient Knowledge Distillation for Face Recognition

This paper introduces SEEKr, a novel approach for improving the efficiency of Knowledge Distillation (KD) in Face Recognition (FR) models. State-of-the-art FR systems achieve strong performance. However, they incur substantial computational costs, particularly during the transfer of knowledge from large, high-capacity teacher models (T) to compact student models (S) using massive training datasets containing millions of samples. SEEKr addresses this challenge by accelerating KD through the selection of a compact yet highly informative subset of the training data. The central insight of SEEKr is to prioritize hard samples, inputs that are challenging for the student, during the distillation process. Emphasizing these samples allows the student to more accurately approximate the teacher's decision function while learning from diverse and informative examples. In contrast, easy samples are shown to contribute marginally to effective knowledge transfer and can be safely excluded without notable performance degradation. By selectively distilling knowledge from hard samples, SEEKr significantly reduces the number of training samples required for KD, thus substantially lowering computational cost. Extensive experiments on multiple large-scale Face Recognition datasets, across diverse teacher architectures and sample selection strategies, demonstrate that SEEKr consistently achieves competitive or superior distillation performance while significantly reducing computational overhead. The proposed method is general, easily integrable into existing KD frameworks, and offers a scalable solution for efficient Face Recognition model training in resource-constrained environments. Code and pretrained models are available at https://github.com/jankolf/SEEKr.