{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T18:08:49Z","timestamp":1774721329628,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T00:00:00Z","timestamp":1626393600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T00:00:00Z","timestamp":1626393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s00371-021-02254-8","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T20:02:30Z","timestamp":1626465750000},"page":"1915-1928","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Beyond view transformation: feature distribution consistent GANs for cross-view gait recognition"],"prefix":"10.1007","volume":"38","author":[{"given":"Yu","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongliang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,16]]},"reference":[{"key":"2254_CR1","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.1007\/s00371-018-01618-x","volume":"35","author":"RP Sharma","year":"2019","unstructured":"Sharma, R.P., Dey, S.: Fingerprint liveness detection using local quality features. Vis. Comput. 35, 1393\u20131410 (2019). https:\/\/doi.org\/10.1007\/s00371-018-01618-x","journal-title":"Vis. Comput."},{"key":"2254_CR2","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.future.2018.06.020","volume":"89","author":"R Gad","year":"2018","unstructured":"Gad, R., Talha, M., El-Latif, A.A.A., Zorkany, M., EL-SAYED, A., EL-Fishawy, N., Muhammad, G.: Iris recognition using multi-algorithmic approaches for cognitive internet of things (CIoT) framework. Future Gener. Comput. Syst. 89, 178\u2013191 (2018). https:\/\/doi.org\/10.1016\/j.future.2018.06.020","journal-title":"Future Gener. Comput. Syst."},{"issue":"3","key":"2254_CR3","doi-asserted-by":"publisher","first-page":"1411","DOI":"10.1007\/s11042-012-1278-7","volume":"71","author":"N Wang","year":"2014","unstructured":"Wang, N., Li, Q., El-Latif, A.A.A., Zhang, T., Niu, X.: Toward accurate localization and high recognition performance for noisy iris images. Multimed. Tools Appl. 71(3), 1411\u20131430 (2014). https:\/\/doi.org\/10.1007\/s11042-012-1278-7","journal-title":"Multimed. Tools Appl."},{"issue":"3","key":"2254_CR4","doi-asserted-by":"publisher","first-page":"2339","DOI":"10.1007\/s11042-013-1551-4","volume":"72","author":"N Wang","year":"2014","unstructured":"Wang, N., Li, Q., El-Latif, A.A.A., Peng, J., Niu, X.: An enhanced thermal face recognition method based on multiscale complex fusion for Gabor coefficients. Multimed. Tools Appl.. 72(3), 2339\u20132358 (2014). https:\/\/doi.org\/10.1007\/s11042-013-1551-4","journal-title":"Multimed. Tools Appl.."},{"key":"2254_CR5","doi-asserted-by":"publisher","unstructured":"Ariyanto, G., Nixon, M. S.: Model-based 3d gait biometrics. In: International Joint Conference on Biometrics, pp. 1\u20137 (2011). https:\/\/doi.org\/10.1109\/IJCB.2011.6117582","DOI":"10.1109\/IJCB.2011.6117582"},{"key":"2254_CR6","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Seah, H. S., Quah, C. K., Ong, A., Jabbar, K.: A multiple camera system with real-time volume reconstruction for articulated skeleton pose tracking. In: International Conference on Multimedia Modeling, pp. 182\u2013192 (2011). https:\/\/doi.org\/10.1007\/978-3-642-17832-0_18","DOI":"10.1007\/978-3-642-17832-0_18"},{"issue":"4","key":"2254_CR7","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1109\/TSMCB.2009.2031091","volume":"40","author":"M Goffredo","year":"2009","unstructured":"Goffredo, M., Bouchrika, I., Carter, J.N., Nixon, M.S.: Self calibrating view-invariant gait biometrics. IEEE Trans. Syst. 40(4), 997\u20131008 (2009). https:\/\/doi.org\/10.1109\/TSMCB.2009.2031091","journal-title":"IEEE Trans. Syst."},{"issue":"5","key":"2254_CR8","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.patrec.2009.11.006","volume":"31","author":"J Lu","year":"2010","unstructured":"Lu, J., Tan, Yap-Peng.: Uncorrelated discriminant simplex analysis for view-invariant gait signal computing. Pattern Recognit. Lett. 31(5), 382\u2013393 (2010). https:\/\/doi.org\/10.1016\/j.patrec.2009.11.006","journal-title":"Pattern Recognit. Lett."},{"key":"2254_CR9","doi-asserted-by":"publisher","unstructured":"Kusakunniran, W., Wu, Q., Li, H., Zhang, J.: Multiple views gait recognition using view transformation model based on optimized gait energy image. In: IEEE International Conference on Computer Vision, pp. 1058\u20131064 (2009) https:\/\/doi.org\/10.1109\/ICCVW.2009.5457587","DOI":"10.1109\/ICCVW.2009.5457587"},{"key":"2254_CR10","doi-asserted-by":"publisher","unstructured":"Kusakunniran, W., Wu, Q., Zhang, J., Li, H.: Support vector regression for multi-view gait recognition based on local motion feature selection. In: Computer Vision and Pattern Recognition, pp. 974\u2013981 (2010) https:\/\/doi.org\/10.1109\/CVPR.2010.5540113","DOI":"10.1109\/CVPR.2010.5540113"},{"key":"2254_CR11","doi-asserted-by":"publisher","unstructured":"Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., Yagi, Y.: Gait recognition using a view transformation model in the frequency domain. In: European Conference on Computer Vision, pp. 151\u2013163 (2006) https:\/\/doi.org\/10.1007\/11744078_12","DOI":"10.1007\/11744078_12"},{"key":"2254_CR12","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.patcog.2015.08.011","volume":"50","author":"X Xing","year":"2016","unstructured":"Xing, X., Wang, K., Yan, T., Lv, Z.: Complete canonical correlation analysis with application to multi-view gait recognition. Pattern Recognit. 50, 107\u2013117 (2016). https:\/\/doi.org\/10.1016\/j.patcog.2015.08.011","journal-title":"Pattern Recognit."},{"issue":"1","key":"2254_CR13","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1109\/TCSVT.2020.2975671","volume":"31","author":"C Xu","year":"2021","unstructured":"Xu, C., Makihara, Y., Li, X., Yagi, Y., Lu, J.: Cross-view gait recognition using pairwise spatial transformer networks. IEEE Trans. Circuits Syst. Video Technol. 31(1), 260\u2013274 (2021). https:\/\/doi.org\/10.1109\/TCSVT.2020.2975671","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"2254_CR14","doi-asserted-by":"publisher","unstructured":"Chao, H., He, Y., Zhang, J., Feng, J.: GaitSet: regarding gait as a set for cross-view gait recognition. In: The National Conference on Artificial Intelligence, pp. 8126\u20138133 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33018126","DOI":"10.1609\/aaai.v33i01.33018126"},{"key":"2254_CR15","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, 106988 (2019). https:\/\/doi.org\/10.1016\/j.patcog.2019.106988","journal-title":"Pattern Recogn."},{"key":"2254_CR16","doi-asserted-by":"publisher","unstructured":"Zhang, P., Wu, Q., Xu, J.: VT-GAN: View transformation GAN for gait recognition across views. In: International Joint Conference on Neural Networks, pp. 14\u201319 (2019) https:\/\/doi.org\/10.1109\/IJCNN.2019.8852258","DOI":"10.1109\/IJCNN.2019.8852258"},{"key":"2254_CR17","doi-asserted-by":"publisher","unstructured":"Zhang, P., Wu, Q., Xu, J.: VN-GAN: Identity-preserved variation normalizing GAN for gait recognition. In: International Joint Conference on Neural Networks, pp. 1\u20138 (2019) https:\/\/doi.org\/10.1109\/IJCNN.2019.8852401","DOI":"10.1109\/IJCNN.2019.8852401"},{"key":"2254_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00524","author":"W Jiang","year":"2020","unstructured":"Jiang, W., Liu, S., Gao, C., Cao, J., He, R., Feng, J., Yan, S.: PSGAN: pose and expression robust spatial-aware GAN for customizable makeup transfer. Comput. Vis. Pattern Recognit. (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00524","journal-title":"Comput. Vis. Pattern Recognit."},{"key":"2254_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-021-02074-w","author":"Z Fang","year":"2021","unstructured":"Fang, Z., Liu, Z., Liu, T., Hung, C.-C., Xiao, J., Feng, G.: Facial expression GAN for voice-driven face generation. Vis. Comput. (2021). https:\/\/doi.org\/10.1007\/s00371-021-02074-w","journal-title":"Vis. Comput."},{"key":"2254_CR20","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412465","author":"F Zhan","year":"2021","unstructured":"Zhan, F., Zhang, C.: Spatial-aware GAN for unsupervised person re- identification. International Conference on Pattern Recognition (2021). https:\/\/doi.org\/10.1109\/ICPR48806.2021.9412465","journal-title":"International Conference on Pattern Recognition"},{"key":"2254_CR21","doi-asserted-by":"publisher","DOI":"10.1109\/IJCB48548.2020.9304910","author":"R Liao","year":"2020","unstructured":"Liao, R., An, W., Yu, S., Li, Z., Huang, Y.: Dense-view GEIs set: view space covering for gait recognition based on dense-view GAN. International Joint Conference on Biometrics (2020). https:\/\/doi.org\/10.1109\/IJCB48548.2020.9304910","journal-title":"International Joint Conference on Biometrics"},{"key":"2254_CR22","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.neucom.2019.02.025","volume":"339","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Song, C., Huang, Y., Wang, Z., Wang, L.: Learning view invariant gait features with two-stream GAN. Neurocomputing 339, 245\u2013254 (2019). https:\/\/doi.org\/10.1016\/j.neucom.2019.02.025","journal-title":"Neurocomputing"},{"key":"2254_CR23","doi-asserted-by":"publisher","DOI":"10.1109\/SLT48900.2021.9383567","author":"H Du","year":"2021","unstructured":"Du, H., Tian, X., Xie, L., Li, H.: Optimizing voice conversion network with cycle consistency loss of speaker identity. IEEE Spoken Language Technology Workshop (2021). https:\/\/doi.org\/10.1109\/SLT48900.2021.9383567","journal-title":"IEEE Spoken Language Technology Workshop"},{"key":"2254_CR24","doi-asserted-by":"crossref","unstructured":"Sanchez, E., Valstar, M.: A recurrent cycle consistency loss for progressive face-to-face synthesis. In: Computer Vision and Pattern Recognition, (2020) https:\/\/arxiv.org\/abs\/2004.07165","DOI":"10.1109\/FG47880.2020.00015"},{"key":"2254_CR25","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: A unified embedding for face recognition and clustering. In: computer vision and pattern recognition, pp. 815\u2013823 (2015) https:\/\/arxiv.org\/abs\/1503.03832","DOI":"10.1109\/CVPR.2015.7298682"},{"issue":"8","key":"2254_CR26","doi-asserted-by":"publisher","first-page":"2034","DOI":"10.1109\/TIFS.2013.2287605","volume":"12","author":"M Hu","year":"2013","unstructured":"Hu, M., Wang, Y., Zhang, Z., Little, J.J., Huang, D.: View-invariant discriminative projection for multi-view gait-based human identification. IEEE Trans. Inf. Forensics Secur. 12(8), 2034\u20132045 (2013). https:\/\/doi.org\/10.1109\/TIFS.2013.2287605","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"2254_CR27","unstructured":"Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. In: Neural information processing systems, pp. 2672\u20132680 (2014) https:\/\/arxiv.org\/abs\/1406.2661"},{"key":"2254_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-021-02133-2","author":"F Bi","year":"2021","unstructured":"Bi, F., Han, J., Tian, Y., Wang, Y.: SSGAN: generative adversarial networks for the stroke segmentation of calligraphic characters. Vis. Comput. (2021). https:\/\/doi.org\/10.1007\/s00371-021-02133-2","journal-title":"Vis. Comput."},{"key":"2254_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-020-01986-3","author":"H Song","year":"2020","unstructured":"Song, H., Wang, M., Zhang, L., Li, Y., Jiang, Z., Yin, G.: S2RGAN: sonar-image super-resolution based on generative adversarial network. Vis. Comput. (2020). https:\/\/doi.org\/10.1007\/s00371-020-01986-3","journal-title":"Vis. Comput."},{"key":"2254_CR30","doi-asserted-by":"publisher","unstructured":"Yu, S., Chen, H., Reyes, E. B. G., Poh, N.: GaitGAN: Invariant gait feature extraction using generative adversarial networks. In: Computer Vision and Pattern Recognition Workshops, pp. 532\u2013539 (2017) https:\/\/doi.org\/10.1109\/CVPRW.2017.80","DOI":"10.1109\/CVPRW.2017.80"},{"key":"2254_CR31","doi-asserted-by":"publisher","unstructured":"Wang, J., song, Y., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., Wu, Y.: Learning fine-grained image similarity with deep ranking. In: Computer Vision and Pattern Recognition, pp. 1386\u20131393 (2014) https:\/\/doi.org\/10.1109\/CVPR.2014.180","DOI":"10.1109\/CVPR.2014.180"},{"key":"2254_CR32","unstructured":"Deng, W., Zheng, L., Ye, Q., Yang, Y., Jiao, J.: Similarity-preserving image-image domain adaptation for person re-identification. In: Computer Vision and Pattern Recognition, pp. 994\u20131003 (2018) https:\/\/arxiv.org\/abs\/1811.10551v2"},{"key":"2254_CR33","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., Efros, A. A.: Image-to-image translation with conditional adversarial networks. In: Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017) https:\/\/arxiv.org\/abs\/1611.07004v1","DOI":"10.1109\/CVPR.2017.632"},{"key":"2254_CR34","unstructured":"Sanchez, E., Valstar, M.: Triple consistency loss for pairing distributions in GAN-based face synthesis. In: Computer Vision and Pattern Recognition, (2018) https:\/\/arxiv.org\/abs\/1811.03492"},{"key":"2254_CR35","doi-asserted-by":"publisher","unstructured":"Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: International Conference on Pattern Recognition, pp. 441\u2013444 (2006) https:\/\/doi.org\/10.1109\/ICPR.2006.67","DOI":"10.1109\/ICPR.2006.67"},{"issue":"5","key":"2254_CR36","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1109\/TIFS.2012","volume":"7","author":"H Iwama","year":"2012","unstructured":"Iwama, H., Okumura, M., Makihara, Y., Yagi, Y.: The OU-ISIR gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Trans. Inf. Forensics Secur. 7(5), 1511\u20131521 (2012). https:\/\/doi.org\/10.1109\/TIFS.2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"4","key":"2254_CR37","doi-asserted-by":"publisher","first-page":"1","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.: Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Trans. Comput. Vis. Appl. 10(4), 1\u201314 (2018). https:\/\/doi.org\/10.1186\/s41074-018-0039-6","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"issue":"2","key":"2254_CR38","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). https:\/\/doi.org\/10.1109\/TPAMI.2006.38","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2254_CR39","unstructured":"Kingma, D., Ba, J.: Adam: A method for stochastic optimization. In: International Conference on Learning Representations, (2014) https:\/\/arxiv.org\/abs\/1412.6980v9"},{"issue":"1","key":"2254_CR40","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1109\/TIFS.2018.2844819","volume":"14","author":"Y He","year":"2019","unstructured":"He, Y., Zhang, J., Shan, H., Wang, L.: Multi-task GANs for view-specific feature learning in gait recognition. IEEE Trans. Inf. Forensics Secur. 14(1), 102\u2013113 (2019). https:\/\/doi.org\/10.1109\/TIFS.2018.2844819","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"2254_CR41","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: Neural Information Processing Systems, pp. 6626\u20136637 (2017) https:\/\/arxiv.org\/abs\/1706.08500"},{"key":"2254_CR42","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Computer Vision and Pattern Recognition, pp. 1\u20139 (2015) https:\/\/arxiv.org\/abs\/1409.4842","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"3","key":"2254_CR43","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.neucom.2021.04.007","volume":"450","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Fu, K., Han, C., Cheng, P.: Identity-and-pose-guided generative adversarial network for face rotation. Neurocomputing 450(3), 33\u201347 (2021). https:\/\/doi.org\/10.1016\/j.neucom.2021.04.007","journal-title":"Neurocomputing"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-021-02254-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-021-02254-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-021-02254-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T08:11:49Z","timestamp":1652170309000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-021-02254-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,16]]},"references-count":43,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["2254"],"URL":"https:\/\/doi.org\/10.1007\/s00371-021-02254-8","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,16]]},"assertion":[{"value":"6 July 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}