{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,6]],"date-time":"2025-07-06T04:40:08Z","timestamp":1751776808522,"version":"3.41.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,7,31]],"date-time":"2018-07-31T00:00:00Z","timestamp":1532995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2018,7,31]],"date-time":"2018-07-31T00:00:00Z","timestamp":1532995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Qoncept, Inc."}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["IPSJ T Comput Vis Appl"],"published-print":{"date-parts":[[2018,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Gait is an important biometric trait for identifying individuals. The use of inputs from multiple or moving cameras offers a promising extension of gait recognition methods. Personal authentication systems at building entrances, for example, can utilize multiple cameras installed at appropriate positions to increase their authentication accuracy. In such cases, it is important to identify effective camera positions to maximize gait recognition performance, but it is not yet clear how different viewpoints affect recognition performance. This study determines the relationship between viewpoint and gait recognition performance to construct standards for selecting an appropriate view for gait recognition using multiple or moving cameras. We evaluate the gait features generated from 3D pedestrian shapes to visualize the directional characteristics of recognition performance.<\/jats:p>","DOI":"10.1186\/s41074-018-0046-7","type":"journal-article","created":{"date-parts":[[2018,7,31]],"date-time":"2018-07-31T11:40:28Z","timestamp":1533037228000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Directional characteristics evaluation of silhouette-based gait recognition"],"prefix":"10.1186","volume":"10","author":[{"given":"Yui","family":"Shigeki","sequence":"first","affiliation":[]},{"given":"Fumio","family":"Okura","sequence":"additional","affiliation":[]},{"given":"Ikuhisa","family":"Mitsugami","sequence":"additional","affiliation":[]},{"given":"Kenichi","family":"Hayashi","sequence":"additional","affiliation":[]},{"given":"Yasushi","family":"Yagi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,7,31]]},"reference":[{"key":"46_CR1","unstructured":"Turk MA, Pentland AP (1991) Face recognition using eigenfaces In: Proc. 1991 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR\u201991), 586\u2013591.. IEEE."},{"key":"46_CR2","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) FaceNet: A unified embedding for face recognition and clustering In: Proc. 2015 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR\u201915), 815\u2013823.. IEEE.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"46_CR3","unstructured":"Nixon MS, Tan T, Chellappa R (2010) Human identification based on Gait. Springer Science & Business Media, NY."},{"issue":"5","key":"46_CR4","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1109\/TIFS.2012.2204253","volume":"7","author":"H Iwama","year":"2012","unstructured":"Iwama H, Okumura M, Makihara Y, Yagi Y (2012) The OU-ISIR gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Trans Inf Forensic Secur 7(5):1511\u20131521.","journal-title":"IEEE Trans Inf Forensic Secur"},{"issue":"2","key":"46_CR5","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1109\/TPAMI.2006.38","volume":"28","author":"J Man","year":"2006","unstructured":"Man J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28(2):316\u2013322.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"46_CR6","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1016\/j.patcog.2010.10.011","volume":"44","author":"TH Lam","year":"2011","unstructured":"Lam TH, Cheung KH, Liu JN (2011) Gait flow image: a silhouette-based gait representation for human identification. Pattern Recognit 44(4):973\u2013987.","journal-title":"Pattern Recognit"},{"key":"46_CR7","doi-asserted-by":"crossref","unstructured":"Urtasun R, Fua P (2004) 3D tracking for gait characterization and recognition In: Proc. Sixth IEEE Int\u2019l Conf. on Automatic Face and Gesture Recognition (FG\u201904), 17\u201322.. IEEE.","DOI":"10.1109\/AFGR.2004.1301503"},{"issue":"1","key":"46_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S1077-3142(03)00008-0","volume":"90","author":"D Cunado","year":"2003","unstructured":"Cunado D, Nixon MS, Carter JN (2003) Automatic extraction and description of human gait models for recognition purposes. Comp Vision Image Underst 90(1):1\u201341.","journal-title":"Comp Vision Image Underst"},{"issue":"4","key":"46_CR9","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1111\/j.1556-4029.2011.01793.x","volume":"56","author":"I Bouchrika","year":"2011","unstructured":"Bouchrika I, Goffredo M, Carter J, Nixon M (2011) On using gait in forensic biometrics. J Forensic Sci 56(4):882\u2013889.","journal-title":"J Forensic Sci"},{"issue":"3","key":"46_CR10","doi-asserted-by":"publisher","first-page":"53","DOI":"10.2197\/ipsjtcva.6.53","volume":"6","author":"T Kimura","year":"2014","unstructured":"Kimura T, Makihara Y, Muramatsu D, Yagi Y (2014) Quality-dependent score-level fusion of face, gait, and the height biometrics. IPSJ Trans Comput Vis Appl 6(3):53\u201357.","journal-title":"IPSJ Trans Comput Vis Appl"},{"key":"46_CR11","doi-asserted-by":"crossref","unstructured":"Jung SU, Nixon MS (2010) On using gait biometrics to enhance face pose estimation In: Proc. Fourth IEEE Int\u2019l Conf. on Biometrics: Theory Applications and Systems (BTAS\u201910), 1\u20136.. IEEE.","DOI":"10.1109\/BTAS.2010.5634473"},{"key":"46_CR12","doi-asserted-by":"crossref","unstructured":"Shakhnarovich G, Lee L, Darrell T (2001) Integrated face and gait recognition from multiple views In: Proc. 2001 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR\u201901), vol. 1.. IEEE.","DOI":"10.1109\/CVPR.2001.990508"},{"key":"46_CR13","doi-asserted-by":"crossref","unstructured":"Seely RD, Samangooei S, Lee M, Carter JN, Nixon MS (2008) The University of Southampton Multi-Biometric Tunnel and introducing a novel 3D gait dataset In: Proc. Second IEEE Int\u2019l Conf. on Biometrics: Theory, Applications and Systems (BTAS\u201908), 1\u20136.. IEEE.","DOI":"10.1109\/BTAS.2008.4699353"},{"issue":"2","key":"46_CR14","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1109\/41.993283","volume":"49","author":"R Canals","year":"2002","unstructured":"Canals R, Roussel A, Famechon JL, Treuillet S (2002) A biprocessor-oriented vision-based target tracking system. IEEE Trans Ind Electron 49(2):500\u2013506.","journal-title":"IEEE Trans Ind Electron"},{"issue":"1","key":"46_CR15","doi-asserted-by":"publisher","first-page":"629102","DOI":"10.1155\/2008\/629102","volume":"2008","author":"X Huang","year":"2008","unstructured":"Huang X, Boulgouris NV (2008) Human gait recognition based on multiview gait sequences. EURASIP J Adv Sig Process 2008(1):629102.","journal-title":"EURASIP J Adv Sig Process"},{"key":"46_CR16","doi-asserted-by":"crossref","unstructured":"Yu S, Tan D, Tan T (2006) Modelling the effect of view angle variation on appearance-based gait recognition In: Proc. Seventh Asian Conf. on Computer Vision (ACCV\u201906), 807\u2013816.. Springer.","DOI":"10.1007\/11612032_81"},{"issue":"2","key":"46_CR17","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TPAMI.2005.39","volume":"27","author":"S Sarkar","year":"2005","unstructured":"Sarkar S, Phillips PJ, Liu Z, Vega IR, Grother P, Bowyer KW (2005) The humanID gait challenge problem: data sets, performance, and analysis. IEEE Trans Pattern Anal Mach Intell 27(2):162\u2013177.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"46_CR18","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s41074-018-0039-6","volume":"10","author":"N Takemura","year":"2018","unstructured":"Takemura N, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2018) Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Trans Comput Vis Appl 10(1):4.","journal-title":"IPSJ Trans Comput Vis Appl"},{"issue":"1","key":"46_CR19","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1109\/TIP.2014.2371335","volume":"24","author":"D Muramatsu","year":"2015","unstructured":"Muramatsu D, Shiraishi A, Makihara Y, Uddin MZ, Yagi Y (2015) Gait-based person recognition using arbitrary view transformation model. IEEE Trans Image Proc 24(1):140\u2013154.","journal-title":"IEEE Trans Image Proc"},{"issue":"7","key":"46_CR20","doi-asserted-by":"publisher","first-page":"1602","DOI":"10.1109\/TCYB.2015.2452577","volume":"46","author":"D Muramatsu","year":"2016","unstructured":"Muramatsu D, Makihara Y, Yagi Y (2016) View transformation model incorporating quality measures for cross-view gait recognition. IEEE Trans Cybern 46(7):1602\u20131615.","journal-title":"IEEE Trans Cybern"},{"key":"46_CR21","doi-asserted-by":"crossref","unstructured":"Wei SE, Ramakrishna V, Kanade T, Sheikh Y (2016) Convolutional pose machines In: Proc. 2016 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR\u201916), 4724\u20134732.. IEEE.","DOI":"10.1109\/CVPR.2016.511"},{"key":"46_CR22","doi-asserted-by":"crossref","unstructured":"Cao Z, Simon T, Wei SE, Sheikh Y (2017) Realtime multi-person 2D pose estimation using part affinity fields In: Proc. 2017 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR\u201917).. IEEE.","DOI":"10.1109\/CVPR.2017.143"},{"key":"46_CR23","doi-asserted-by":"crossref","unstructured":"Bogo F, Kanazawa A, Lassner C, Gehler P, Romero J, Black MJ (2016) Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image In: Proc. 2016 European Conf. on Computer Vision (ECCV\u201916), 561\u2013578.. Springer.","DOI":"10.1007\/978-3-319-46454-1_34"},{"key":"46_CR24","doi-asserted-by":"crossref","unstructured":"Lin G, Milan A, Shen C, Reid I (2017) RefineNet: Multi-path refinement networks with identity mappings for high-resolution semantic segmentation In: Proc. 2017 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR\u201917).. IEEE.","DOI":"10.1109\/CVPR.2017.549"},{"key":"46_CR25","doi-asserted-by":"crossref","unstructured":"Xu C, Makihara Y, Li X, Yagi Y, Lu J (2016) Speed invariance vs. stability: cross-speed gait recognition using single-support gait energy image In: Proc. 2016 Asian Conf. on Computer Vision (ACCV\u201916), 52\u201367.. Springer.","DOI":"10.1007\/978-3-319-54184-6_4"},{"key":"46_CR26","doi-asserted-by":"crossref","unstructured":"Li X, Makihara Y, Xu C, Muramatsu D, Yagi Y, Ren M (2016) Gait energy response function for clothing-invariant gait recognition In: Proc. 2016 Asian Conf. on Computer Vision (ACCV\u201916), 257\u2013272.. Springer.","DOI":"10.1007\/978-3-319-54184-6_16"},{"key":"46_CR27","doi-asserted-by":"crossref","unstructured":"Makihara Y, Suzuki A, Muramatsu D, Li X, Yagi Y (2017) Joint intensity and spatial metric learning for robust gait recognition In: Proc. 2017 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR\u201917).. IEEE.","DOI":"10.1109\/CVPR.2017.718"},{"key":"46_CR28","doi-asserted-by":"crossref","unstructured":"Muramatsu D, Shiraishi A, Makihara Y, Yagi Y (2012) Arbitrary view transformation model for gait person authentication In: Proc. IEEE Fifth Int\u2019l Conf. on Biometrics: Theory, Applications and Systems (BTAS\u201912), 85\u201390.. IEEE.","DOI":"10.1109\/BTAS.2012.6374561"},{"key":"46_CR29","doi-asserted-by":"publisher","first-page":"94","DOI":"10.2197\/ipsjtcva.7.94","volume":"7","author":"T Ikeda","year":"2015","unstructured":"Ikeda T, Mitsugami I, Yagi Y (2015) Depth-based gait authentication for practical sensor settings. IPSJ Trans Comput Vis Appl 7:94\u201398.","journal-title":"IPSJ Trans Comput Vis Appl"},{"key":"46_CR30","unstructured":"Takemura N, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2018) On input\/output architectures for convolutional neural network-based cross-view gait recognition. IEEE Trans Circ Syst Video Technol 28(1)."}],"container-title":["IPSJ Transactions on Computer Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s41074-018-0046-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s41074-018-0046-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s41074-018-0046-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,6]],"date-time":"2025-07-06T03:59:03Z","timestamp":1751774343000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s41074-018-0046-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,31]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["46"],"URL":"https:\/\/doi.org\/10.1186\/s41074-018-0046-7","relation":{},"ISSN":["1882-6695"],"issn-type":[{"type":"electronic","value":"1882-6695"}],"subject":[],"published":{"date-parts":[[2018,7,31]]},"assertion":[{"value":"15 February 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Publisher\u2019s Note"}}],"article-number":"10"}}