Learning Joint Gait Representation via Quintuplet Loss Minimization

Kaihao Zhang, Wenhan Luo, Lin Ma, Wei Liu, Hongdong Li; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4700-4709


Gait recognition is an important biometric method popularly used in video surveillance, where the task is to identify people at a distance by their walking patterns from video sequences. Most of the current successful approaches for gait recognition either use a pair of gait images to form a cross-gait representation or rely on a single gait image for unique-gait representation. These two types of representations emperically complement one another. In this paper, we propose a new Joint Unique-gait and Cross-gait Network (JUCNet), to combine the advantages of unique-gait representation with that of cross-gait representation, leading to an significantly improved performance. Another key contribution of this paper is a novel quintuplet loss function, which simultaneously increases the inter-class differences by pushing representations extracted from different subjects apart and decreases the intra-class variations by pulling representations extracted from the same subject together. Experiments show that our method achieves the state-of-the-art performance tested on standard benchmark datasets, demonstrating its superiority over existing methods.

Related Material

author = {Zhang, Kaihao and Luo, Wenhan and Ma, Lin and Liu, Wei and Li, Hongdong},
title = {Learning Joint Gait Representation via Quintuplet Loss Minimization},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}