Open Access

ARTICLE

Gait Recognition via Cross Walking Condition Constraint

Runsheng Wang¹, Hefei Ling^1,*, Ping Li¹, Yuxuan Shi¹, Lei Wu¹, Jialie Shen²

1 HuaZhong University of Science and Technology, Wuhan, 430074, China
2 Queen’s University, Belfast, BT7 1NN, UK

* Corresponding Author: Hefei Ling. Email:

Computers, Materials & Continua 2021, 68(3), 3045-3060. https://doi.org/10.32604/cmc.2021.017275

Received 23 January 2021; Accepted 25 February 2021; Issue published 06 May 2021

Abstract

Gait recognition is a biometric technique that captures human walking pattern using gait silhouettes as input and can be used for long-term recognition. Recently proposed video-based methods achieve high performance. However, gait covariates or walking conditions, i.e., bag carrying and clothing, make the recognition of intra-class gait samples hard. Advanced methods simply use triplet loss for metric learning, which does not take the gait covariates into account. For alleviating the adverse influence of gait covariates, we propose cross walking condition constraint to explicitly consider the gait covariates. Specifically, this approach designs center-based and pair-wise loss functions to decrease discrepancy of intra-class gait samples under different walking conditions and enlarge the distance of inter-class gait samples under the same walking condition. Besides, we also propose a video-based strong baseline model of high performance by applying simple yet effective tricks, which have been validated in other individual recognition fields. With the proposed baseline model and loss functions, our method achieves the state-of-the-art performance.

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Keywords

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