{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T05:44:28Z","timestamp":1761198268370,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031476365"},{"type":"electronic","value":"9783031476372"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-47637-2_18","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T20:01:39Z","timestamp":1699128099000},"page":"230-242","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Unified Convolutional Neural Network for\u00a0Gait Recognition"],"prefix":"10.1007","author":[{"given":"Sonam","family":"Nahar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sagar","family":"Narsingani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yash","family":"Patel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Bashir, K., Xiang, T., Gong, S.: Gait recognition using gait entropy image. In: 3rd International Conference on Imaging for Crime Detection and Prevention, ICDP 2009, pp. 1\u20136 (2009)","DOI":"10.1049\/ic.2009.0230"},{"issue":"13","key":"18_CR2","doi-asserted-by":"publisher","first-page":"2052","DOI":"10.1016\/j.patrec.2010.05.027","volume":"31","author":"K Bashir","year":"2010","unstructured":"Bashir, K., Xiang, T., Gong, S.: Gait recognition without subject cooperation. Pattern Recogn. Lett. 31(13), 2052\u20132060 (2010)","journal-title":"Pattern Recogn. Lett."},{"issue":"4","key":"18_CR3","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1109\/TPAMI.2018.2818162","volume":"41","author":"K Cao","year":"2019","unstructured":"Cao, K., Jain, A.K.: Automated latent fingerprint recognition. IEEE Trans. Pattern Anal. Mach. Intell. 41(4), 788\u2013800 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR4","unstructured":"Chao, H., He, Y., Zhang, J., Feng, J.: GaitSet: regarding gait as a set for cross-view gait recognition. CoRR abs\/1811.06186 (2018)"},{"issue":"C","key":"18_CR5","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.patcog.2015.11.016","volume":"53","author":"X Chen","year":"2016","unstructured":"Chen, X., Xu, J.: Uncooperative gait recognition. Pattern Recogn. 53(C), 116\u2013129 (2016)","journal-title":"Pattern Recogn."},{"issue":"2","key":"18_CR6","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1109\/TPAMI.2006.38","volume":"28","author":"J Han","year":"2006","unstructured":"Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Trans. Pattern Anal. Mach. Intell. 28(2), 316\u2013322 (2006)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR7","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, NIPS 2012, vol. 1, pp. 1097\u20131105. Curran Associates Inc., Red Hook, NY, USA (2012)"},{"issue":"2","key":"18_CR8","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1109\/TIP.2013.2294552","volume":"23","author":"W Kusakunniran","year":"2014","unstructured":"Kusakunniran, W., Wu, Q., Zhang, J., Li, H., Wang, L.: Recognizing gaits across views through correlated motion co-clustering. IEEE Trans. Image Process. 23(2), 696\u2013709 (2014)","journal-title":"IEEE Trans. Image Process."},{"key":"18_CR9","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1016\/j.patcog.2010.10.011","volume":"44","author":"T Lam","year":"2011","unstructured":"Lam, T., Cheung, K., Liu, J.: Gait flow image: a silhouette-based gait representation for human identification. Pattern Recogn. 44, 973\u2013987 (2011)","journal-title":"Pattern Recogn."},{"key":"18_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/11744078_12","volume-title":"Computer Vision \u2013 ECCV 2006","author":"Y Makihara","year":"2006","unstructured":"Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Gait recognition using a view transformation model in the frequency domain. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 151\u2013163. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11744078_12"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Mansur, A., Makihara, Y., Muramatsu, D., Yagi, Y.: Cross-view gait recognition using view-dependent discriminative analysis. In: IEEE International Joint Conference on Biometrics, pp. 1\u20138 (2014)","DOI":"10.1109\/BTAS.2014.6996272"},{"key":"18_CR12","unstructured":"Murray., M.: Gait as a total pattern of movement (1967)"},{"key":"18_CR13","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/j.patcog.2017.05.021","volume":"72","author":"K Nguyen","year":"2017","unstructured":"Nguyen, K., Fookes, C., Jillela, R., Sridharan, S., Ross, A.: Long range iris recognition: a survey. Pattern Recogn. 72, 123\u2013143 (2017)","journal-title":"Pattern Recogn."},{"issue":"2","key":"18_CR14","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TPAMI.2005.39","volume":"27","author":"S Sarkar","year":"2005","unstructured":"Sarkar, S., Phillips, P., Liu, Z., Vega, I., Grother, P., Bowyer, K.: The humanid gait challenge problem: data sets, performance, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 162\u2013177 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR15","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1049\/iet-bmt.2019.0001","volume":"9","author":"A Sepas-Moghaddam","year":"2020","unstructured":"Sepas-Moghaddam, A.: Face recognition: a novel multi-level taxonomy based survey. IET Biometrics 9, 58\u201367 (2020)","journal-title":"IET Biometrics"},{"issue":"1","key":"18_CR16","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1109\/TPAMI.2022.3151865","volume":"45","author":"A Sepas-Moghaddam","year":"2023","unstructured":"Sepas-Moghaddam, A., Etemad, A.: Deep gait recognition: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 264\u2013284 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR17","doi-asserted-by":"crossref","unstructured":"Shiraga, K., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: GEINet: view-invariant gait recognition using a convolutional neural network. In: 2016 International Conference on Biometrics (ICB), pp. 1\u20138 (2016)","DOI":"10.1109\/ICB.2016.7550060"},{"key":"18_CR18","doi-asserted-by":"publisher","first-page":"70497","DOI":"10.1109\/ACCESS.2018.2879896","volume":"6","author":"JP Singh","year":"2018","unstructured":"Singh, J.P., Jain, S., Arora, S., Singh, U.P.: Vision-based gait recognition: a survey. IEEE Access 6, 70497\u201370527 (2018)","journal-title":"IEEE Access"},{"issue":"C","key":"18_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.106988","volume":"96","author":"C Song","year":"2019","unstructured":"Song, C., Huang, Y., Huang, Y., Jia, N., Wang, L.: GaitNet: an end-to-end network for gait based human identification. Pattern Recogn. 96(C), 106988 (2019)","journal-title":"Pattern Recogn."},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701\u20131708 (2014)","DOI":"10.1109\/CVPR.2014.220"},{"issue":"11","key":"18_CR21","doi-asserted-by":"publisher","first-page":"2164","DOI":"10.1109\/TPAMI.2011.260","volume":"34","author":"C Wang","year":"2012","unstructured":"Wang, C., Zhang, J., Wang, L., Pu, J., Yuan, X.: Human identification using temporal information preserving gait template. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2164\u20132176 (2012)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR22","unstructured":"Wang, J., Chen, Y., Hao, S., Peng, X., Hu, L.: Deep learning for sensor-based activity recognition: a survey. CoRR abs\/1707.03502 (2017)"},{"issue":"2","key":"18_CR23","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1109\/TPAMI.2016.2545669","volume":"39","author":"Z Wu","year":"2017","unstructured":"Wu, Z., Huang, Y., Wang, L., Wang, X., Tan, T.: A comprehensive study on cross-view gait based human identification with deep CNNs. IEEE Trans. Pattern Anal. Mach. Intell. 39(2), 209\u2013226 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR24","unstructured":"Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 4, pp. 441\u2013444 (2006)"},{"issue":"3","key":"18_CR25","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.imavis.2005.10.007","volume":"25","author":"R Zhang","year":"2007","unstructured":"Zhang, R., Vogler, C., Metaxas, D.: Human gait recognition at sagittal plane. Image Vis. Comput. 25(3), 321\u2013330 (2007)","journal-title":"Image Vis. Comput."},{"key":"18_CR26","unstructured":"Zhao, G., Liu, G., Li, H., Pietikainen, M.: 3D gait recognition using multiple cameras. In: 7th International Conference on Automatic Face and Gesture Recognition, FGR 2006, pp. 529\u2013534 (2006)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47637-2_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T20:08:04Z","timestamp":1699128484000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47637-2_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031476365","9783031476372"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47637-2_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kitakyushu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"acpr2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ericlab.org\/acpr2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"164","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"93","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"57% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}