Robust clothing-independent gait recognition using hybrid part-based gait features
Recently, gait has been gathering extensive interest for the non-fungible position in applications. Although various methods have been proposed for gait recognition, most of them can only attain an excellent recognition performance when the probe and gallery gaits are in a similar condition. Once ex...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2022-05-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-996.pdf |
| _version_ | 1811244693229404160 |
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| author | Zhipeng Gao Junyi Wu Tingting Wu Renyu Huang Anguo Zhang Jianqiang Zhao |
| author_facet | Zhipeng Gao Junyi Wu Tingting Wu Renyu Huang Anguo Zhang Jianqiang Zhao |
| author_sort | Zhipeng Gao |
| collection | DOAJ |
| description | Recently, gait has been gathering extensive interest for the non-fungible position in applications. Although various methods have been proposed for gait recognition, most of them can only attain an excellent recognition performance when the probe and gallery gaits are in a similar condition. Once external factors (e.g., clothing variations) influence people’s gaits and changes happen in human appearances, a significant performance degradation occurs. Hence, in our article, a robust hybrid part-based spatio-temporal feature learning method is proposed for gait recognition to handle this cloth-changing problem. First, human bodies are segmented into the affected and non/less unaffected parts based on the anatomical studies. Then, a well-designed network is proposed in our method to formulate our required hybrid features from the non/less unaffected body parts. This network contains three sub-networks, aiming to generate features independently. Each sub-network emphasizes individual aspects of gait, hence an effective hybrid gait feature can be created through their concatenation. In addition, temporal information can be used as complement to enhance the recognition performance, a sub-network is specifically proposed to establish the temporal relationship between consecutive short-range frames. Also, since local features are more discriminative than global features in gait recognition, in this network a sub-network is specifically proposed to generate features of local refined differences. The effectiveness of our proposed method has been evaluated by experiments on the CASIA Gait Dataset B and OU-ISIR Treadmill Gait Dataset B. Related experiments illustrate that compared with other gait recognition methods, our proposed method can achieve a prominent result when handling this cloth-changing gait recognition problem. |
| first_indexed | 2024-04-12T14:29:43Z |
| format | Article |
| id | doaj.art-516176d744f44a808daf0ddff080634a |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-12T14:29:43Z |
| publishDate | 2022-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-516176d744f44a808daf0ddff080634a2022-12-22T03:29:19ZengPeerJ Inc.PeerJ Computer Science2376-59922022-05-018e99610.7717/peerj-cs.996Robust clothing-independent gait recognition using hybrid part-based gait featuresZhipeng Gao0Junyi Wu1Tingting Wu2Renyu Huang3Anguo Zhang4Jianqiang Zhao5Xiamen Meiya Pico Information Co., Ltd., Xiamen, Fujian, ChinaXiamen Meiya Pico Information Co., Ltd., Xiamen, Fujian, ChinaXiamen Meiya Pico Information Co., Ltd., Xiamen, Fujian, ChinaXiamen Meiya Pico Information Co., Ltd., Xiamen, Fujian, ChinaCollege of Mathematics and Data Science, Minjiang University, Fuzhou, ChinaXiamen Meiya Pico Information Co., Ltd., Xiamen, Fujian, ChinaRecently, gait has been gathering extensive interest for the non-fungible position in applications. Although various methods have been proposed for gait recognition, most of them can only attain an excellent recognition performance when the probe and gallery gaits are in a similar condition. Once external factors (e.g., clothing variations) influence people’s gaits and changes happen in human appearances, a significant performance degradation occurs. Hence, in our article, a robust hybrid part-based spatio-temporal feature learning method is proposed for gait recognition to handle this cloth-changing problem. First, human bodies are segmented into the affected and non/less unaffected parts based on the anatomical studies. Then, a well-designed network is proposed in our method to formulate our required hybrid features from the non/less unaffected body parts. This network contains three sub-networks, aiming to generate features independently. Each sub-network emphasizes individual aspects of gait, hence an effective hybrid gait feature can be created through their concatenation. In addition, temporal information can be used as complement to enhance the recognition performance, a sub-network is specifically proposed to establish the temporal relationship between consecutive short-range frames. Also, since local features are more discriminative than global features in gait recognition, in this network a sub-network is specifically proposed to generate features of local refined differences. The effectiveness of our proposed method has been evaluated by experiments on the CASIA Gait Dataset B and OU-ISIR Treadmill Gait Dataset B. Related experiments illustrate that compared with other gait recognition methods, our proposed method can achieve a prominent result when handling this cloth-changing gait recognition problem.https://peerj.com/articles/cs-996.pdfGait recognitionPart-basedSpatio-temporal feature learningClothing-independent |
| spellingShingle | Zhipeng Gao Junyi Wu Tingting Wu Renyu Huang Anguo Zhang Jianqiang Zhao Robust clothing-independent gait recognition using hybrid part-based gait features PeerJ Computer Science Gait recognition Part-based Spatio-temporal feature learning Clothing-independent |
| title | Robust clothing-independent gait recognition using hybrid part-based gait features |
| title_full | Robust clothing-independent gait recognition using hybrid part-based gait features |
| title_fullStr | Robust clothing-independent gait recognition using hybrid part-based gait features |
| title_full_unstemmed | Robust clothing-independent gait recognition using hybrid part-based gait features |
| title_short | Robust clothing-independent gait recognition using hybrid part-based gait features |
| title_sort | robust clothing independent gait recognition using hybrid part based gait features |
| topic | Gait recognition Part-based Spatio-temporal feature learning Clothing-independent |
| url | https://peerj.com/articles/cs-996.pdf |
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