Generalized Inter-class Loss for Gait Recognition

TL;DR

This paper introduces a generalized inter-class loss for gait recognition that enhances inter-class feature separation by dynamically adjusting margins and promoting uniformity, leading to state-of-the-art results.

Contribution

It proposes a novel inter-class loss that considers sample and class-level distributions, improving gait recognition accuracy across different networks.

Findings

Significant performance improvements on CASIA-B and OUMVLP datasets.

Achieves state-of-the-art gait recognition results.

Method is adaptable to various gait recognition networks.

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.