{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:29:16Z","timestamp":1760232556236,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research Fundation of Education Bureau of Liaoning Province","award":["LZD202001","2021JH1\/10400029"],"award-info":[{"award-number":["LZD202001","2021JH1\/10400029"]}]},{"name":"Science and Technology Project of Department of Science &amp; Technology of Liaoning Province","award":["LZD202001","2021JH1\/10400029"],"award-info":[{"award-number":["LZD202001","2021JH1\/10400029"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Although gait recognition has been greatly improved by efforts from many researchers in recent years, its performance is still unsatisfactory due to the lack of gait information under the real scenariowhere only one or two images may be used for recognition. In this paper, a new gait recognition framework is brought about which can combine the long-short-term attention modules on silhouette images over the whole sequence and the real human physiological information calculated by a monocular image. The contributions of this work include the following: (1) Fusing the global long-term attention (GLTA) and local short-term attention (LSTA) over the whole query sequence to improve the gait recognition accuracy, where both the short-term gait feature (from two or three frames) and long-term feature (from the whole sequence) are extracted; (2) presenting a method to calculate the real personal static and dynamic physiological features through a single monocular image; (3) by efficiently applying the human physiological information, a new physiological feature extraction (PFE) network is proposed to concatenate the physiological information with silhouette for gait recognition. Through the experiments between the CASIA-B and Multi-state Gait datasets, the effectiveness and efficiency of the proposed method are proven. Under three different walking conditions of the CASIA-B dataset, the mean accuracy of rank-1 in our method is up to 89.6%, and in the Multi-state Gait dataset, wearing different clothes, the mean accuracy of rank-1 in our method is 2.4% higher than the other works.<\/jats:p>","DOI":"10.3390\/s22228779","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T02:32:16Z","timestamp":1668479536000},"page":"8779","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Gait Recognition by Combining the Long-Short-Term Attention Network and Personal Physiological Features"],"prefix":"10.3390","volume":"22","author":[{"given":"Chunsheng","family":"Hua","sequence":"first","affiliation":[{"name":"Institute of Intelligent Robot and Pattern Recognition, College of Information, Liaoning University, No. 66 Chongshan Middle Road, Huanggu District, Shenyang 110036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjie","family":"Pan","sequence":"additional","affiliation":[{"name":"College of Information, Liaoning University, Shenyang 110036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Endocrinology and Metabolism, The Fourth Affiliated Hospital of China Medical University, Shenyang 110096, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6262-4642","authenticated-orcid":false,"given":"Zhibo","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenyang Contain Electronic Technology Co., Ltd., Shenyang 110167, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, J., and Zheng, N. (2007, January 2\u20135). Gait history image: A novel temporal template for gait recognition. Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, Beijing, China.","DOI":"10.1109\/ICME.2007.4284737"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Singh, S., and Biswas, K. (2009, January 16\u201320). Biometric gait recognition with carrying and clothing variants. Proceedings of the International Conference on Pattern Recognition and Machine Intelligence, Delhi, India.","DOI":"10.1007\/978-3-642-11164-8_72"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2071","DOI":"10.1109\/TIFS.2015.2445315","article-title":"Cross-speed gait recognition using speed-invariant gait templates and globality\u2013locality preserving projections","volume":"10","author":"Huang","year":"2015","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shiraga, K., Makihara, Y., Muramatsu, D., Echigo, T., and Yagi, Y. (2016, January 13\u201316). Geinet: View-invariant gait recognition using a convolutional neural network. Proceedings of the 2016 International Conference on Biometrics (ICB), Halmstad, Sweden.","DOI":"10.1109\/ICB.2016.7550060"},{"key":"ref_5","unstructured":"Chao, H., He, Y., Zhang, J., and Feng, J. (February, January 27). Gaitset: Regarding gait as a set for cross-view gait recognition. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Fan, C., Peng, Y., Cao, C., Liu, X., Hou, S., Chi, J., Huang, Y., Li, Q., and He, Z. (2020, January 14\u201319). Gaitpart: Temporal part-based model for gait recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01423"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liao, R., Cao, C., Garcia, E.B., Yu, S., and Huang, Y. (2017, January 28\u201329). Pose-based temporal-spatial network (PTSN) for gait recognition with carrying and clothing variations. Proceedings of the Chinese Conference on Biometric Recognition, Shenzhen, China.","DOI":"10.1007\/978-3-319-69923-3_51"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/TPAMI.2016.2545669","article-title":"A comprehensive study on cross-view gait based human identification with deep cnns","volume":"39","author":"Wu","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wolf, T., Babaee, M., and Rigoll, G. (2016, January 25\u201328). Multi-view gait recognition using 3D convolutional neural networks. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533144"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Thapar, D., Nigam, A., Aggarwal, D., and Agarwal, P. (2018, January 11\u201312). VGR-net: A view invariant gait recognition network. Proceedings of the 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA), Singapore.","DOI":"10.1109\/ISBA.2018.8311475"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107069","DOI":"10.1016\/j.patcog.2019.107069","article-title":"A model-based gait recognition method with body pose and human prior knowledge","volume":"98","author":"Liao","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_12","unstructured":"Feng, Y., Li, Y., and Luo, J. (2016, January 4\u20138). Learning effective gait features using LSTM. Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yu, S., Chen, H., Garcia Reyes, E.B., and Poh, N. (2017, January 21\u201326). Gaitgan: Invariant gait feature extraction using generative adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.80"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1109\/TPAMI.2006.38","article-title":"Individual recognition using gait energy image","volume":"28","author":"Han","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015, January 7\u201313). Learning spatiotemporal features with 3d convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/78.650093","article-title":"Bidirectional recurrent neural networks","volume":"45","author":"Schuster","year":"1997","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"BenAbdelkader, C., Cutler, R., and Davis, L. (2002, January 1). View-invariant estimation of height and stride for gait recognition. Proceedings of the International Workshop on Biometric Authentication, Copenhagen, Denmark.","DOI":"10.1007\/3-540-47917-1_16"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1109\/LSP.2010.2040927","article-title":"Gait recognition using geometric features and soft biometrics","volume":"17","author":"Moustakas","year":"2010","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_20","first-page":"441","article-title":"A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition","volume":"Volume 4","author":"Yu","year":"2006","journal-title":"Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906)"},{"key":"ref_21","unstructured":"Jocher, G. (2021, May 01). Ultralytics. Yolov5. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s11263-008-0137-5","article-title":"Putting objects in perspective","volume":"80","author":"Hoiem","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cao, Z., Simon, T., Wei, S.E., and Sheikh, Y. (2017, January 21\u201326). Realtime multi-person 2d pose estimation using part affinity fields. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.143"},{"key":"ref_24","unstructured":"Hermans, A., Beyer, L., and Leibe, B. (2017). In defense of the triplet loss for person re-identification. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Tran, L., Yin, X., Atoum, Y., Liu, X., Wan, J., and Wang, N. (2019, January 16\u201320). Gait recognition via disentangled representation learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00484"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_27","first-page":"1","article-title":"Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition","volume":"10","author":"Takemura","year":"2018","journal-title":"IPSJ Trans. Comput. Vis. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8779\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:17:41Z","timestamp":1760145461000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8779"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,14]]},"references-count":27,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22228779"],"URL":"https:\/\/doi.org\/10.3390\/s22228779","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,11,14]]}}}