Temporal feature learning based on attention mechanism for gait recognition

Lihua Fu; Huixian Wu

doi:10.1117/12.3011450

8 November 2023 Temporal feature learning based on attention mechanism for gait recognition

Lihua Fu, Huixian Wu

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129230B (2023) https://doi.org/10.1117/12.3011450
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

Most of current gait recognition models do not consider different influence of the existing gait profiles on recognition results. In addition, most of the existing gait recognition models ignore temporal information of gait profiles. We propose a temporal feature learning based on attention mechanism for gait recognition. Firstly, key feature subnet module is designed to extract key features that have great influence on recognition results, which allows the model to extract more discriminative information. Then, temporal feature extraction module is given to focus on temporal information, and extract long temporal features, short temporal features and frame level features. After that, temporal modeling module is put forward to model the relationship between temporal features based on attention mechanism. Finally, the effectiveness of the proposed method is verified by experiments.

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

Citation Download Citation

Lihua Fu and Huixian Wu "Temporal feature learning based on attention mechanism for gait recognition", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129230B (8 November 2023); https://doi.org/10.1117/12.3011450

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KEYWORDS

Gait analysis

Feature extraction

Modeling

Ablation

Convolution

Matrices

Neural networks

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