Generalized Inter-class Loss for Gait Recognition

• URL: http://arxiv.org/abs/2210.06779v1
• Date: Thu, 13 Oct 2022 06:44:53 GMT
• Title: Generalized Inter-class Loss for Gait Recognition
• Authors: Weichen Yu, Hongyuan Yu, Yan Huang, Liang Wang
• Abstract summary: Gait recognition is a unique biometric technique that can be performed at a long distance non-cooperatively. Previous gait works focus more on minimizing the intra-class variance while ignoring the significance in constraining inter-class variance. We propose a generalized inter-class loss which resolves the inter-class variance from both sample-level feature distribution and class-level feature distribution.
• Score: 11.15855312510806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gait recognition is a unique biometric technique that can be performed at a long distance non-cooperatively and has broad applications in public safety and intelligent traffic systems. Previous gait works focus more on minimizing the intra-class variance while ignoring the significance in constraining inter-class variance. To this end, we propose a generalized inter-class loss which resolves the inter-class variance from both sample-level feature distribution and class-level feature distribution. Instead of equal penalty strength on pair scores, the proposed loss optimizes sample-level inter-class feature distribution by dynamically adjusting the pairwise weight. Further, in class-level distribution, generalized inter-class loss adds a constraint on the uniformity of inter-class feature distribution, which forces the feature representations to approximate a hypersphere and keep maximal inter-class variance. In addition, the proposed method automatically adjusts the margin between classes which enables the inter-class feature distribution to be more flexible. The proposed method can be generalized to different gait recognition networks and achieves significant improvements. We conduct a series of experiments on CASIA-B and OUMVLP, and the experimental results show that the proposed loss can significantly improve the performance and achieves the state-of-the-art performances.

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