ShrinkTeaNet: Million-scale Lightweight Face Recognition via Shrinking Teacher-Student Networks

TL;DR

ShrinkTeaNet introduces a teacher-student learning framework with a novel Angular Distillation Loss, enabling the training of lightweight face recognition networks that maintain high accuracy on large-scale, open-set face recognition tasks.

Contribution

The paper proposes ShrinkTeaNet, a new teacher-student paradigm with Angular Distillation Loss for robust, compact face recognition models suitable for large-scale open-set scenarios.

Findings

Achieved 99.77% accuracy on LFW with a lightweight model.

Demonstrated high performance on MegaFace with one million distractors.

Produced models with significantly fewer parameters while maintaining competitive accuracy.

Abstract

Large-scale face recognition in-the-wild has been recently achieved matured performance in many real work applications. However, such systems are built on GPU platforms and mostly deploy heavy deep network architectures. Given a high-performance heavy network as a teacher, this work presents a simple and elegant teacher-student learning paradigm, namely ShrinkTeaNet, to train a portable student network that has significantly fewer parameters and competitive accuracy against the teacher network. Far apart from prior teacher-student frameworks mainly focusing on accuracy and compression ratios in closed-set problems, our proposed teacher-student network is proved to be more robust against open-set problem, i.e. large-scale face recognition. In addition, this work introduces a novel Angular Distillation Loss for distilling the feature direction and the sample distributions of the teacher's hypersphere to its student. Then ShrinkTeaNet framework can efficiently guide the student's learning process with the teacher's knowledge presented in both intermediate and last stages of the feature embedding. Evaluations on LFW, CFP-FP, AgeDB, IJB-B and IJB-C Janus, and MegaFace with one million distractors have demonstrated the efficiency of the proposed approach to learn robust student networks which have satisfying accuracy and compact sizes. Our ShrinkTeaNet is able to support the light-weight architecture achieving high performance with 99.77% on LFW and 95.64% on large-scale Megaface protocols.