Improving Disentangled Representation Learning for Gait Recognition using Group Supervision
2
Issued Date
2022-01-01
Resource Type
ISSN
15209210
eISSN
19410077
Scopus ID
2-s2.0-85129365207
Journal Title
IEEE Transactions on Multimedia
Rights Holder(s)
SCOPUS
Bibliographic Citation
IEEE Transactions on Multimedia (2022)
Suggested Citation
Yao L., Kusakunniran W., Zhang P., Wu Q., Zhang J. Improving Disentangled Representation Learning for Gait Recognition using Group Supervision. IEEE Transactions on Multimedia (2022). doi:10.1109/TMM.2022.3171961 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84396
Title
Improving Disentangled Representation Learning for Gait Recognition using Group Supervision
Author(s)
Other Contributor(s)
Abstract
In decades, gait has been gathering extensive interest for 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 the feature adaptability and independency, a disentanglement swap module is specifically adopted during our encode-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 to obtain a more excellent recognition result, despite fewer gait frames being utilized.