A gait recognition method based on deep learning and attention transformer

Zhili Lu; Xiuqing Mao

doi:10.1117/12.3012246

21 December 2023 A gait recognition method based on deep learning and attention transformer

Zhili Lu, Xiuqing Mao

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 129701B (2023) https://doi.org/10.1117/12.3012246
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

Gait recognition is a biometric technology with important applications in identity verification and crime prevention. However, developing robust and accurate algorithms remains a challenge due to various factors such as environmental interference and clothing changes. Current gait recognition methods are categorized into template-based and sequence-based approaches, both of which have limitations. Template-based methods are simple but lose temporal information, while sequence-based methods preserve temporal information but are sensitive to unnecessary sequential constraints. Furthermore, these methods are not robust to various sources of noise and interference and often require a large number of gait frames for accurate recognition. Therefore, new approaches are needed to improve the accuracy and efficiency of gait recognition. We proposed the method, named Adaptive GaitSet, addresses these limitations by introducing an attention mechanism that allows the model to adaptively focus on important gait features. This method not only improves the accuracy and efficiency of gait recognition, but also enhances the robustness of the model to variations in walking conditions and sources of noise and interference. Experimental results show that the proposed method achieves the best performance on benchmark dataset.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhili Lu and Xiuqing Mao "A gait recognition method based on deep learning and attention transformer", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 129701B (21 December 2023); https://doi.org/10.1117/12.3012246

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