Pose-Invariant Face Recognition via Adaptive Angular Distillation

Authors

  • Zhenduo Zhang Tsinghua University
  • Yongru Chen Tsinghua University
  • Wenming Yang Tsinghua University
  • Guijin Wang Tsinghua University
  • Qingmin Liao Tsinghua Univeristy

DOI:

https://doi.org/10.1609/aaai.v36i3.20249

Keywords:

Computer Vision (CV)

Abstract

Pose-invariant face recognition is a practically useful but challenging task. This paper introduces a novel method to learn pose-invariant feature representation without normalizing profile faces to frontal ones or learning disentangled features. We first design a novel strategy to learn pose-invariant feature embeddings by distilling the angular knowledge of frontal faces extracted by teacher network to student network, which enables the handling of faces with large pose variations. In this way, the features of faces across variant poses can cluster compactly for the same person to create a pose-invariant face representation. Secondly, we propose a Pose-Adaptive Angular Distillation loss to mitigate the negative effect of uneven distribution of face poses in the training dataset to pay more attention to the samples with large pose variations. Extensive experiments on two challenging benchmarks (IJB-A and CFP-FP) show that our approach consistently outperforms the existing methods.

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Published

2022-06-28

How to Cite

Zhang, Z., Chen, Y., Yang, W., Wang, G., & Liao, Q. (2022). Pose-Invariant Face Recognition via Adaptive Angular Distillation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(3), 3390-3398. https://doi.org/10.1609/aaai.v36i3.20249

Issue

Section

AAAI Technical Track on Computer Vision III