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Improving Disentangled Representation Learning for Gait Recognition Using Group Supervision
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AI
DOI：10.1109/TMM.2022.3171961.png)
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Abstract
En 中文
For decades, gait has been gathering extensive interest due to the advantage that it can be measured from a distance without physical contact. However, for image/video-based gait recognition, its performance can be remarkably influenced by exterior factors, such as viewing angles and clothing changes. Thus, in this paper, a group-supervised disentangled representation learning network is proposed for gait recognition to extract features invariant to these factors. First, sequences are explicitly disentangled into pose, gait, appearance, and view features through a generic encoder-decoder framework. To ensure feature adaptability and independency, a disentanglement swap module is specifically adopted during our encoder-decoder process through a series of swap operations based on the feature attributes. Following the feature disentanglement, a disentanglement aggregation module is also specially proposed for pose, gait, and appearance features to enhance their effectiveness. Finally, the enhanced three features are concatenated together for gait recognition. Relevant experiments certify that compared with other disentangled representation learning-based gait recognition methods, our proposed method enables a more excellent recognition result, despite fewer gait frames being utilized.
Keywords:
Gait recognition
deep learning
disentangled representation learning
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