{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T19:27:38Z","timestamp":1773775658540,"version":"3.50.1"},"reference-count":177,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Case study program"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future.<\/jats:p>","DOI":"10.3390\/s23104875","type":"journal-article","created":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T10:17:26Z","timestamp":1684405046000},"page":"4875","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Person Recognition Based on Deep Gait: A Survey"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6846-1610","authenticated-orcid":false,"given":"Md.","family":"Khaliluzzaman","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, Bangladesh"},{"name":"Department of Computer Science and Engineering, International Islamic University Chittagong, Chattogram 4318, Bangladesh"}]},{"given":"Ashraf","family":"Uddin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7345-0999","authenticated-orcid":false,"given":"Kaushik","family":"Deb","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4578-952X","authenticated-orcid":false,"given":"Md Junayed","family":"Hasan","sequence":"additional","affiliation":[{"name":"National Subsea Centre, Robert Gordon University, Aberdeen AB10 7AQ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mar\u00edn-Jim\u00e9nez, M.J., Castro, F.M., Guil, N., De la Torre, F., and Medina-Carnicer, R. (2017, January 17\u201320). Deep Multi-Task Learning for Gait-Based Biometrics. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296252"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1049\/iet-bmt.2019.0001","article-title":"Face Recognition: A Novel Multi-Level Taxonomy Based Survey","volume":"9","author":"Pereira","year":"2020","journal-title":"IET Biom."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1093\/gerona\/62.9.1010","article-title":"Physical Fatigue Affects Gait Characteristics in Older Persons","volume":"62","author":"Helbostad","year":"2007","journal-title":"J. Gerontol. Ser. A Biol. Sci. Med. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.patcog.2017.05.021","article-title":"Long Range Iris Recognition: A Survey","volume":"72","author":"Nguyen","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"19143","DOI":"10.1109\/ACCESS.2019.2896880","article-title":"Speech recognition using deep neural networks: A systematic review","volume":"7","author":"Nassif","year":"2019","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/TBIOM.2021.3112540","article-title":"TypeNet: Deep learning keystroke biometrics","volume":"4","author":"Acien","year":"2021","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"ref_7","unstructured":"Makihara, Y., Nixon, M.S., and Yagi, Y. (2020). Computer Vision: A Reference Guide, Springer."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rani, V., and Kumar, M. (2023). Human Gait Recognition: A Systematic Review. Multimed. Tools Appl., 1\u201335.","DOI":"10.1007\/s11042-023-15079-5"},{"key":"ref_9","first-page":"264","article-title":"Deep Gait Recognition: A Survey","volume":"45","author":"Etemad","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Liang, J., Fan, C., Hou, S., Shen, C., Huang, Y., and Yu, S. (2022, January 23\u201327). Gaitedge: Beyond Plain End-to-End Gait Recognition for Better Practicality. Proceedings of the Computer Vision\u2013ECCV 2022: 17th European Conference, Tel Aviv, Israel. Part V.","DOI":"10.1007\/978-3-031-20065-6_22"},{"key":"ref_11","first-page":"44","article-title":"Hand written signature recognition & verification using neural network","volume":"3","author":"Kumar","year":"2013","journal-title":"Int. J. Adv. Res. Comput. Sci. Softw. Eng."},{"key":"ref_12","unstructured":"Ghalleb, A.E.K., Slamia, R.B., and Amara, N.E.B. (2016, January 21\u201323). Contribution to the Fusion of Soft Facial and Body Biometrics for Remote People Identification. Proceedings of the 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), Monastir, Tunisia."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3136","DOI":"10.1109\/TBME.2019.2900863","article-title":"The classification of minor gait alterations using wearable sensors and deep learning","volume":"66","author":"Turner","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3362","DOI":"10.3390\/s140203362","article-title":"Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications","volume":"14","year":"2014","journal-title":"Sensors"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"2833","DOI":"10.1007\/s11042-013-1574-x","article-title":"A comprehensive review of past and present vision-based techniques for gait recognition. Multimed","volume":"72","author":"Lee","year":"2014","journal-title":"Tools Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s41074-019-0054-2","article-title":"Gait-Based Age Estimation Using Multi-Stage Convolutional Neural Network","volume":"11","author":"Sakata","year":"2019","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1109\/TIFS.2010.2069560","article-title":"Gait-Based Human Age Estimation","volume":"5","author":"Lu","year":"2010","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Makihara, Y., Okumura, M., Iwama, H., and Yagi, Y. (2011, January 11\u201313). Gait-Based Age Estimation Using a Whole-Generation Gait Database. Proceedings of the 2011 International Joint Conference on Biometrics (IJCB), Washington, DC, USA.","DOI":"10.1109\/IJCB.2011.6117531"},{"key":"ref_20","unstructured":"Oliveira, E.L., Lima, C.A., and Peres, S.M. (2016, January 17\u201320). Fusion of Face and Gait for Biometric Recognition: Systematic Literature Review. Proceedings of the XII Brazilian Symposium on Information Systems on Brazilian Symposium on Information Systems: Information Systems in the Cloud Computing Era, Porto Alegre, Brazil."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"S90","DOI":"10.1016\/j.injury.2019.11.011","article-title":"Gait Analysis\u2013Available Platforms for Outcome Assessment","volume":"51","author":"Brand","year":"2020","journal-title":"Injury"},{"key":"ref_22","first-page":"513","article-title":"Visual Analysis of Gait as a Cue to Identity","volume":"13","author":"Stevenage","year":"1999","journal-title":"Appl. Cogn. Psychol. Off. J. Soc. Appl. Res. Mem. Cogn."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2302","DOI":"10.1109\/JBHI.2019.2938111","article-title":"From Emotions to Mood Disorders: A Survey on Gait Analysis Methodology","volume":"23","author":"Deligianni","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sigal, L., Fleet, D.J., Troje, N.F., and Livne, M. (2010, January 5\u201311). Human Attributes from 3D Pose Tracking. Proceedings of the Computer Vision\u2013ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece. Part III.","DOI":"10.1007\/978-3-642-15558-1_18"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.robot.2016.07.004","article-title":"Identification of a Specific Person Using Color, Height, and Gait Features for a Person Following Robot","volume":"84","author":"Koide","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, C., Gong, S., Loy, C.C., and Lin, X. (2012, January 7\u201313). Person Re-Identification: What Features Are Important?. Proceedings of the Computer Vision\u2013ECCV 2012. Workshops and Demonstrations, Florence, Italy. Part I.","DOI":"10.1007\/978-3-642-33863-2_39"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2018.02.010","article-title":"Deep Learning for Sensor-Based Activity Recognition: A Survey","volume":"119","author":"Wang","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1109\/TSMCB.2010.2044040","article-title":"Recognition of Affect Based on Gait Patterns","volume":"40","author":"Karg","year":"2010","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"115057","DOI":"10.1016\/j.eswa.2021.115057","article-title":"Multi-View Gait Recognition System Using Spatio-Temporal Features and Deep Learning","volume":"179","author":"Gul","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"14213","DOI":"10.1109\/JSEN.2021.3066473","article-title":"Fusion of Multi-Sensor-Based Biomechanical Gait Analysis Using Vision and Wearable Sensor","volume":"21","author":"Bijalwan","year":"2021","journal-title":"IEEE Sensors J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yan, C., Zhang, B., and Coenen, F. (2015, January 14\u201316). Multi-Attributes Gait Identification by Convolutional Neural Networks. Proceedings of the 2015 8th International Congress on Image and Signal Processing (CISP), Shenyang, China.","DOI":"10.1109\/CISP.2015.7407957"},{"key":"ref_32","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_33","unstructured":"Yu, S., Tan, D., and Tan, T. (2006, January 20\u201324). A Framework for Evaluating the Effect of View Angle, Clothing, and Carrying Condition on Gait Recognition. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Washington, DC, USA."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","unstructured":"Yao, L., Kusakunniran, W., Wu, Q., Zhang, J., and Tang, Z. (2018, January 10\u201313). Robust CNN-Based Gait Verification and Identification Using Skeleton Gait Energy Image. Proceedings of the 2018 Digital Image Computing: Techniques and Applications (DICTA), Canberra, Australia.","DOI":"10.1109\/DICTA.2018.8615802"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Tran, L., Yin, X., Atoum, Y., Liu, X., Wan, J., and Wang, N. (2019, January 15\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_37","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_38","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1049\/iet-bmt.2018.5046","article-title":"Pose-Based Deep Gait Recognition","volume":"8","author":"Sokolova","year":"2019","journal-title":"IET Biom."},{"key":"ref_39","first-page":"124","article-title":"View-Invariant Gait Recognition with Attentive Recurrent Learning of Partial Representations","volume":"3","author":"Etemad","year":"2020","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"ref_40","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 13\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_41","doi-asserted-by":"crossref","unstructured":"Lin, B., Zhang, S., and Bao, F. (2020, January 12\u201316). Gait Recognition with Multiple-Temporal-Scale 3D Convolutional Neural Network. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA.","DOI":"10.1145\/3394171.3413861"},{"key":"ref_42","unstructured":"Li, X., Makihara, Y., Xu, C., Yagi, Y., Yu, S., and Ren, M. (December, January 30). End-to-End Model-Based Gait Recognition. Proceedings of the Asian Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hou, S., Cao, C., Liu, X., and Huang, Y. (2020, January 23\u201328). Gait Lateral Network: Learning Discriminative and Compact Representations for Gait Recognition. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK. Part IX.","DOI":"10.1007\/978-3-030-58545-7_22"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2713","DOI":"10.1007\/s00371-021-02245-9","article-title":"Residual Connection-Based Graph Convolutional Neural Networks for Gait Recognition","volume":"37","author":"Shopon","year":"2021","journal-title":"Vis. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"107868","DOI":"10.1016\/j.patcog.2021.107868","article-title":"Multi-Task Learning for Gait-Based Identity Recognition and Emotion Recognition Using Attention Enhanced Temporal Graph Convolutional Network","volume":"114","author":"Sheng","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Huang, Z., Xue, D., Shen, X., Tian, X., Li, H., Huang, J., and Hua, X.S. (2021, January 10\u201317). 3D Local Convolutional Neural Networks for Gait Recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01465"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5452","DOI":"10.1109\/TIFS.2021.3132579","article-title":"UGaitNet: Multimodal Gait Recognition with Missing Input Modalities","volume":"16","author":"Castro","year":"2021","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_48","first-page":"1","article-title":"Combining the Silhouette and Skeleton Data for Gait Recognition","volume":"1","author":"Wang","year":"2023","journal-title":"Proceedings"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"54572","DOI":"10.1109\/ACCESS.2022.3176873","article-title":"Multiview Gait Recognition on Unconstrained Path Using Graph Convolutional Neural Network","volume":"10","author":"Shopon","year":"2022","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Mogan, J.N., Lee, C.P., Lim, K.M., and Muthu, K.S. (2022). Gait-ViT: Gait Recognition with Vision Transformer. Sensors, 22.","DOI":"10.3390\/s22197362"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","article-title":"OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields","volume":"43","author":"Cao","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Fang, H.S., Xie, S., Tai, Y.W., and Lu, C. (2017, January 22\u201329). Rmpe: Regional Multi-Person Pose Estimation. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.256"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3230633","article-title":"A Survey on Gait Recognition","volume":"51","author":"Wan","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1049\/iet-bmt.2018.5063","article-title":"Robust Gait Recognition: A Comprehensive Survey","volume":"8","author":"Rida","year":"2019","journal-title":"IET Biom."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3243043","article-title":"Gait-Based Person Re-identification: A Survey","volume":"52","author":"Nambiar","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3340293","article-title":"A Survey on Gait Recognition via Wearable Sensors","volume":"52","author":"Marsico","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"70497","DOI":"10.1109\/ACCESS.2018.2879896","article-title":"Vision-Based Gait Recognition: A Survey","volume":"6","author":"Singh","year":"2018","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2018.01.007","article-title":"Biometric Recognition by Gait: A Survey of Modalities and Features","volume":"167","author":"Connor","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"756","DOI":"10.19026\/rjaset.12.2751","article-title":"A Survey of Gait Recognition Based on Skeleton Model for Human Identification","volume":"12","author":"Nordin","year":"2016","journal-title":"Res. J. Appl. Sci. Eng. Technol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s41074-018-0039-6","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."},{"key":"ref_61","first-page":"2801","article-title":"CASIA-E: A Large Comprehensive Dataset for Gait Recognition","volume":"45","author":"Song","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1007\/978-3-642-19309-5_34","article-title":"Gait Analysis of Gender and Age Using a Large-Scale Multi-View Gait Database","volume":"Volume 10","author":"Makihara","year":"2011","journal-title":"Proceedings of the Computer Vision\u2014ACCV 2010: 10th Asian Conference on Computer Vision"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"53","DOI":"10.2197\/ipsjtcva.4.53","article-title":"The OU-ISIR Gait Database Comprising the Treadmill Dataset","volume":"4","author":"Makihara","year":"2012","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1511","DOI":"10.1109\/TIFS.2012.2204253","article-title":"The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition","volume":"7","author":"Iwama","year":"2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s41074-018-0041-z","article-title":"The OU-ISIR Large Population Gait Database with Real-Life Carried Object and Its Performance Evaluation","volume":"10","author":"Uddin","year":"2018","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1109\/TPAMI.2003.1251144","article-title":"Silhouette Analysis-Based Gait Recognition for Human Identification","volume":"25","author":"Wang","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_67","unstructured":"Tan, D., Huang, K., Yu, S., and Tan, T. (2006, January 20\u201324). Efficient Night Gait Recognition Based on Template Matching. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Washington, DC, USA."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.patcog.2019.04.023","article-title":"A Comprehensive Study on Gait Biometrics Using a Joint CNN-Based Method","volume":"93","author":"Zhang","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Tsuji, A., Makihara, Y., and Yagi, Y. (2010, January 13\u201318). Silhouette Transformation Based on Walking Speed for Gait Identification. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540144"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1016\/j.patcog.2009.12.020","article-title":"Clothing-Invariant Gait Identification Using Part-Based Clothing Categorization and Adaptive Weight Control","volume":"43","author":"Hossain","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1109\/TBIOM.2020.3008862","article-title":"Performance Evaluation of Model-Based Gait on Multi-View Very Large Population Database with Pose Sequences","volume":"2","author":"An","year":"2020","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Hofmann, M., Bachmann, S., and Rigoll, G. (2012, January 23\u201327). 2.5D Gait Biometrics Using the Depth Gradient Histogram Energy Image. Proceedings of the 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), Arlington, VA, USA.","DOI":"10.1109\/BTAS.2012.6374606"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2990","DOI":"10.1109\/TCSVT.2021.3095290","article-title":"RPNet: Gait Recognition with Relationships between Each Body-Parts","volume":"32","author":"Qin","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_74","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_75","doi-asserted-by":"crossref","unstructured":"Sharif, M.I., Khan, M.A., Alqahtani, A., Nazir, M., Alsubai, S., Binbusayyis, A., and Dama\u0161evi\u010dius, R. (2022). Deep Learning and Kurtosis-Controlled, Entropy-Based Framework for Human Gait Recognition Using Video Sequences. Electronics, 11.","DOI":"10.3390\/electronics11030334"},{"key":"ref_76","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_77","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_78","first-page":"282","article-title":"Learning and Relearning in Boltzmann Machines","volume":"Volume 1","author":"Hinton","year":"1986","journal-title":"Parallel Distributed Processing: Explorations in the Microstructure of Cognition"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative Adversarial Networks: An Overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.neucom.2017.02.006","article-title":"Invariant Feature Extraction for Gait Recognition Using Only One Uniform Model","volume":"239","author":"Yu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_81","unstructured":"Sabour, S., Frosst, N., and Hinton, G.E. (2017). Advances in Neural Information Processing Systems, ACM Digital Library."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., and Bronstein, M.M. (2017, January 21\u201326). Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.576"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"63164","DOI":"10.1109\/ACCESS.2018.2876890","article-title":"Gait-Based Human Identification by Combining Shallow Convolutional Neural Network-Stacked Long Short-Term Memory and Deep Convolutional Neural Network","volume":"6","author":"Batchuluun","year":"2018","journal-title":"IEEE Access"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.patcog.2018.10.019","article-title":"GaitGANv2: Invariant Gait Feature Extraction Using Generative Adversarial Networks","volume":"87","author":"Yu","year":"2019","journal-title":"Pattern Recognition"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"19196","DOI":"10.1109\/ACCESS.2020.2967845","article-title":"Feature Extraction Using an RNN Autoencoder for Skeleton-Based Abnormal Gait Recognition","volume":"8","author":"Jun","year":"2020","journal-title":"IEEE Access"},{"key":"ref_86","first-page":"3436","article-title":"Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks","volume":"44","author":"Wang","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.1109\/TPAMI.2019.2891584","article-title":"Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning","volume":"41","author":"Han","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1109\/TBIOM.2021.3074963","article-title":"Set Residual Network for Silhouette-Based Gait Recognition","volume":"3","author":"Hou","year":"2021","journal-title":"IEEE Trans. Biom. Behav. Identity Sci."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"108519","DOI":"10.1016\/j.patcog.2021.108519","article-title":"A Unified Perspective of Classification-Based Loss and Distance-Based Loss for Cross-View Gait Recognition","volume":"125","author":"Han","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Hou, S., Liu, X., Cao, C., and Huang, Y. (2022). Gait Quality Aware Network: Toward the Interpretability of Silhouette-Based Gait Recognition. IEEE Trans. Neural Netw. Learn. Syst., 1\u201311.","DOI":"10.1109\/TNNLS.2022.3154723"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Dou, H., Zhang, P., Zhao, Y., Dong, L., Qin, Z., and Li, X. GaitMPL: Gait Recognition with Memory-Augmented Progressive Learning. IEEE Trans. Image Process., 2022.","DOI":"10.1109\/TIP.2022.3164543"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"108453","DOI":"10.1016\/j.patcog.2021.108453","article-title":"GaitSlice: A Gait Recognition Model Based on Spatio-Temporal Slice Features","volume":"124","author":"Li","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_94","unstructured":"Agarap, A.F. (2018). Deep Learning Using Rectified Linear Units (ReLU). arXiv."},{"key":"ref_95","first-page":"111","article-title":"Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks","volume":"1","author":"Karlik","year":"2011","journal-title":"Int. J. Artif. Intell. Expert Syst."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"103845","DOI":"10.1016\/j.dsp.2022.103845","article-title":"Attention-Based Gait Recognition Network with Novel Partial Representation PGOFI Based on Prior Motion Information","volume":"13","author":"Xu","year":"2023","journal-title":"Digit. Signal Process."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-020-00387-6","article-title":"Analysis and Best Parameters Selection for Person Recognition Based on Gait Model Using CNN Algorithm and Image Augmentation","volume":"8","author":"Saleh","year":"2021","journal-title":"J. Big Data"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"3653","DOI":"10.1007\/s11227-020-03409-5","article-title":"Gait Recognition for Person Re-Identification","volume":"77","author":"Elharrouss","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Li, X., Makihara, Y., Xu, C., and Yagi, Y. (2021, January 10\u201317). End-to-End Model-Based Gait Recognition Using Synchronized Multi-View Pose Constraint. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00456"},{"key":"ref_100","first-page":"3467","article-title":"GaitSet: Cross-View Gait Recognition through Utilizing Gait as a Deep Set","volume":"44","author":"Chao","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"106988","DOI":"10.1016\/j.patcog.2019.106988","article-title":"GaitNet: An End-to-End Network for Gait-Based Human Identification","volume":"96","author":"Song","year":"2019","journal-title":"Pattern Recognit."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1109\/TIFS.2018.2844819","article-title":"Multi-Task GANs for View-Specific Feature Learning in Gait Recognition","volume":"14","author":"He","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Zhang, K., Luo, W., Ma, L., Liu, W., and Li, H. (2019, January 15\u201320). Learning Joint Gait Representation via Quintuplet Loss Minimization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00483"},{"key":"ref_104","unstructured":"Li, N., Zhao, X., and Ma, C. (2020). JointsGait: A Model-Based Gait Recognition Method Based on Gait Graph Convolutional Networks and Joints Relationship Pyramid Mapping. arXiv."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"14410","DOI":"10.1109\/ACCESS.2018.2807385","article-title":"Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey","volume":"6","author":"Akhtar","year":"2018","journal-title":"IEEE Access"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.neucom.2019.02.025","article-title":"Learning View Invariant Gait Features with Two-Stream GAN","volume":"339","author":"Wang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Zhang, P., Wu, Q., and Xu, J. (2019, January 14\u201319). VT-GAN: View Transformation GAN for Gait Recognition Across Views. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852258"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Zhang, P., Wu, Q., and Xu, J. (2019, January 14\u201319). VN-GAN: Identity-Preserved Variation Normalizing GAN for Gait Recognition. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852401"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"107376","DOI":"10.1016\/j.patcog.2020.107376","article-title":"Gait Recognition Invariant to Carried Objects Using Alpha Blending Generative Adversarial Networks","volume":"105","author":"Li","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_110","first-page":"237","article-title":"Gait Recognition Based on Model-Based Methods and Deep Belief Networks","volume":"8","author":"Benouis","year":"2016","journal-title":"Int. J. Biom."},{"key":"ref_111","first-page":"art00015","article-title":"Deep Network for Analyzing Gait Patterns in Low Resolution Video Towards Threat Identification","volume":"2016","author":"Nair","year":"2016","journal-title":"Electron. Imaging"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.jvcir.2019.01.023","article-title":"Gait Recognition Based on Capsule Network","volume":"59","author":"Xu","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Wang, Y., Bilinski, P., Bremond, F., and Dantcheva, A. (2020, January 1\u20135). Imaginator: Conditional Spatio-Temporal GAN for Video Generation. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093492"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Wu, Y., Hou, J., Su, Y., Wu, C., Huang, M., and Zhu, Z. (2020, January 12\u201314). Gait Recognition Based on Feedback Weight Capsule Network. Proceedings of the 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Chongqing, China.","DOI":"10.1109\/ITNEC48623.2020.9084819"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Sepas-Moghaddam, A., Ghorbani, S., Troje, N.F., and Etemad, A. (2021, January 10\u201315). Gait Recognition Using Multi-Scale Partial Representation Transformation with Capsules. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412517"},{"key":"ref_116","unstructured":"Lipton, Z.C., Berkowitz, J., and Elkan, C. (2015). A Critical Review of Recurrent Neural Networks for Sequence Learning. arXiv."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s12021-018-9362-4","article-title":"Learning Efficient Spatial-Temporal Gait Features with Deep Learning for Human Identification","volume":"16","author":"Liu","year":"2018","journal-title":"Neuroinformatics"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.patrec.2018.05.004","article-title":"TGLSTM: A Time Based Graph Deep Learning Approach to Gait Recognition","volume":"126","author":"Battistone","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1109\/TIP.2009.2020535","article-title":"A Study on Gait-Based Gender Classification","volume":"18","author":"Yu","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative Adversarial Networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/TSMCC.2007.913886","article-title":"Gait Components and Their Application to Gender Recognition","volume":"38","author":"Li","year":"2008","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"013010","DOI":"10.1117\/1.JEI.27.1.013010","article-title":"View-Invariant Gait Recognition Method by Three-Dimensional Convolutional Neural Network","volume":"27","author":"Xing","year":"2018","journal-title":"J. Electron. Imaging"},{"key":"ref_123","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_124","doi-asserted-by":"crossref","unstructured":"Lin, B., Zhang, S., and Yu, X. (2021, January 10\u201317). Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.01438"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"3102","DOI":"10.1109\/TIFS.2019.2912577","article-title":"Joint Intensity Transformer Network for Gait Recognition Robust Against Clothing and Carrying Status","volume":"14","author":"Li","year":"2019","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Li, X., Makihara, Y., Xu, C., Yagi, Y., and Ren, M. (2020, January 13\u201319). Gait Recognition via Semi-Supervised Disentangled Representation Learning to Identity and Covariate Features. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01332"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Teepe, T., Gilg, J., Herzog, F., H\u00f6rmann, S., and Rigoll, G. (2022, January 19\u201324). Towards a Deeper Understanding of Skeleton-Based Gait Recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual Event.","DOI":"10.1109\/CVPRW56347.2022.00163"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.patrec.2022.03.019","article-title":"Distilled Light GaitSet: Towards Scalable Gait Recognition","volume":"157","author":"Song","year":"2022","journal-title":"Pattern Recognit. Lett."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Teepe, T., Khan, A., Gilg, J., Herzog, F., H\u00f6rmann, S., and Rigoll, G. (2021, January 19\u201322). Gaitgraph: Graph Convolutional Network for Skeleton-Based Gait Recognition. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506717"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1109\/TIP.2019.2926208","article-title":"Cross-View Gait Recognition by Discriminative Feature Learning","volume":"29","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Liu, D., Ye, M., Li, X., Zhang, F., and Lin, L. (2016, January 19\u201322). Memory-Based Gait Recognition. Proceedings of the BMVC, York, UK.","DOI":"10.5244\/C.30.82"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Li, S., Liu, W., Ma, H., and Zhu, S. (2018, January 23\u201327). Beyond View Transformation: Cycle-Consistent Global and Partial Perception GAN for View-Invariant Gait Recognition. Proceedings of the 2018 IEEE International Conference on Multimedia and Expo (ICME), San Diego, CA, USA.","DOI":"10.1109\/ICME.2018.8486484"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1109\/TPAMI.2020.2998790","article-title":"On Learning Disentangled Representations for Gait Recognition","volume":"44","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"106273","DOI":"10.1016\/j.knosys.2020.106273","article-title":"SpiderNet: A Spiderweb Graph Neural Network for Multi-View Gait Recognition","volume":"206","author":"Zhao","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"1960","DOI":"10.1109\/TMM.2015.2477681","article-title":"Learning Representative Deep Features for Image Set Analysis","volume":"17","author":"Wu","year":"2015","journal-title":"IEEE Trans. Multimed."},{"key":"ref_136","doi-asserted-by":"crossref","unstructured":"Zhang, C., Liu, W., Ma, H., and Fu, H. (2016, January 20\u201325). Siamese Neural Network Based Gait Recognition for Human Identification. Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China.","DOI":"10.1109\/ICASSP.2016.7472194"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.cviu.2017.10.004","article-title":"Improved Gait Recognition Based on Specialized Deep Convolutional Neural Network","volume":"164","author":"Alotaibi","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Li, C., Min, X., Sun, S., Lin, W., and Tang, Z. (2017). DeepGait: A Learning Deep Convolutional Representation for View-Invariant Gait Recognition Using Joint Bayesian. Appl. Sci., 7.","DOI":"10.3390\/app7030210"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"2708","DOI":"10.1109\/TCSVT.2017.2760835","article-title":"On Input\/Output Architectures for Convolutional Neural Network-Based Cross-View Gait Recognition","volume":"29","author":"Takemura","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Castro, F.M., Mar\u00edn-Jim\u00e9nez, M.J., Guil, N., L\u00f3pez-Tapia, S., and de la Blanca, N.P. (2017, January 20\u201322). Evaluation of CNN Architectures for Gait Recognition Based on Optical Flow Maps. Proceedings of the 2017 International Conference of the Biometrics Special Interest Group (BIOSIG), Darmstadt, Germany.","DOI":"10.23919\/BIOSIG.2017.8053503"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Tong, S., Ling, H., Fu, Y., and Wang, D. (2017, January 23\u201327). Cross-View Gait Identification with Embedded Learning. Proceedings of the on Thematic Workshops of ACM Multimedia 2017, New York, NY, USA.","DOI":"10.1145\/3126686.3126753"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Liao, R., Cao, C., Garcia, E.B., Yu, S., and Huang, Y. (2017, January 16\u201318). Pose-based temporal-spatial network (PTSN) for gait recognition with carrying and clothing variations. Proceedings of the Chinese Conference on Biometric Recognition, Beijing, China.","DOI":"10.1007\/978-3-319-69923-3_51"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"57583","DOI":"10.1109\/ACCESS.2018.2874073","article-title":"Multi-View Gait Recognition Based on a Spatial-Temporal Deep Neural Network","volume":"6","author":"Tong","year":"2018","journal-title":"IEEE Access"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.jvcir.2018.06.019","article-title":"Feedback Weight Convolutional Neural Network for Gait Recognition","volume":"55","author":"Wu","year":"2018","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"An, W., Liao, R., Yu, S., Huang, Y., and Yuen, P.C. (2018, January 11\u201312). Improving Gait Recognition with 3D Pose Estimation. Proceedings of the Biometric Recognition: 13th Chinese Conference, CCBR 2018, Urumqi, China.","DOI":"10.1007\/978-3-319-97909-0_15"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.sysarc.2019.01.002","article-title":"Gait Recognition with Cross-Domain Transfer Networks","volume":"93","author":"Tong","year":"2019","journal-title":"J. Syst. Archit."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.patrec.2019.04.010","article-title":"Cross-View Gait Recognition Based on a Restrictive Triplet Network","volume":"125","author":"Tong","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Sokolova, A., and Konushin, A. (2019, January 20\u201323). View Resistant Gait Recognition. Proceedings of the 3rd International Conference on Video and Image Processing, Shanghai, China.","DOI":"10.1145\/3376067.3376083"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"2361","DOI":"10.1109\/TMM.2019.2900134","article-title":"Attentive Spatial-Temporal Summary Networks for Feature Learning in Irregular Gait Recognition","volume":"21","author":"Li","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"14275","DOI":"10.1007\/s00521-019-04524-y","article-title":"Gait Recognition Using Multichannel Convolutional Neural Networks","volume":"32","author":"Wang","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"7275","DOI":"10.1007\/s00521-019-04256-z","article-title":"Cross-View Gait Recognition through Ensemble Learning","volume":"32","author":"Wang","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_152","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_153","doi-asserted-by":"crossref","first-page":"2917","DOI":"10.1007\/s11042-019-08509-w","article-title":"Gait Feature Extraction and Gait Classification Using Two-Branch CNN","volume":"79","author":"Wang","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"1950027","DOI":"10.1142\/S0129065719500278","article-title":"Human Gait Recognition Based on Frame-by-Frame Gait Energy Images and Convolutional Long Short-Term Memory","volume":"30","author":"Wang","year":"2020","journal-title":"Int. J. Neural Syst."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/TCSVT.2020.2975671","article-title":"Cross-View Gait Recognition Using Pairwise Spatial Transformer Networks","volume":"31","author":"Xu","year":"2021","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"6065","DOI":"10.1007\/s11042-020-09935-x","article-title":"Non-Local Gait Feature Extraction and Human Identification","volume":"80","author":"Wang","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1007\/s11042-020-09777-7","article-title":"Gait Classification through CNN-Based Ensemble Learning","volume":"80","author":"Wang","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_158","first-page":"1105","article-title":"Gait Recognition Based on GF-CNN and Metric Learning","volume":"16","author":"Wen","year":"2020","journal-title":"J. Inf. Process. Syst."},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Mehmood, A., Khan, M.A., Sharif, M., Khan, S.A., Shaheen, M., Saba, T., Riaz, N., and Ashraf, I. (2020). Prosperous Human Gait Recognition: An End-to-End System Based on Pre-Trained CNN Features Selection. Multimed. Tools Appl., 1\u201321.","DOI":"10.1007\/s11042-020-08928-0"},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Yousef, R.N., Khalil, A.T., Samra, A.S., and Ata, M.M. (2023). Model-Based and Model-Free Deep Features Fusion for High-Performance Human Gait Recognition. J. Supercomput., 1\u201338.","DOI":"10.1007\/s11227-023-05156-9"},{"key":"ref_161","unstructured":"Pan, J., Sun, H., Wu, Y., Yin, S., and Wang, S. (December, January 30). Optimization of GaitSet for Gait Recognition. Proceedings of the Asian Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_162","unstructured":"Zhang, P., Song, Z., and Xing, X. (December, January 30). Multi-Grid Spatial and Temporal Feature Fusion for Human Identification at a Distance. Proceedings of the Asian Conference on Computer Vision (ACCV), Kyoto, Japan."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"75381","DOI":"10.1109\/ACCESS.2020.2986554","article-title":"Flexible Gait Recognition Based on Flow Regulation of Local Features between Key Frames","volume":"8","author":"Huang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Su, J., Zhao, Y., and Li, X. (2020, January 4\u20138). Deep Metric Learning Based on Center-Ranked Loss for Gait Recognition. Proceedings of the ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9054645"},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Liao, R., An, W., Yu, S., Li, Z., and Huang, Y. (October, January 28). Dense-View GEIs Set: View Space Covering for Gait Recognition Based on Dense-View GAN. Proceedings of the 2020 IEEE International Joint Conference on Biometrics (IJCB), Houston, TX, USA.","DOI":"10.1109\/IJCB48548.2020.9304910"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.neucom.2020.02.048","article-title":"Learning 3D Spatiotemporal Gait Feature by Convolutional Network for Person Identification","volume":"397","author":"Hua","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s12530-021-09397-y","article-title":"3D Convolution Neural Network-Based Person Identification Using Gait Cycles","volume":"12","author":"Supraja","year":"2021","journal-title":"Evol. Syst."},{"key":"ref_168","unstructured":"Wu, X., An, W., Yu, S., Guo, W., and Garc\u00eda, E.B. (2019, January 26\u201329). Spatial-Temporal Graph Attention Network for Video-Based Gait Recognition. Proceedings of the Pattern Recognition: 5th Asian Conference (ACPR 2019), Auckland, New Zealand. Revised Selected Papers, Part II."},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Kadry, S., Parwekar, P., Dama\u0161evi\u010dius, R., Mehmood, A., Khan, J.A., and Naqvi, S.R. (2021). Human Gait Analysis for Osteoarthritis Prediction: A Framework of Deep Learning and Kernel Extreme Learning Machine. Complex Intell. Syst., 1\u201319.","DOI":"10.1007\/s40747-020-00244-2"},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Wang, Z., Tang, C., Su, H., and Li, X. (November, January 29). Model-Based Gait Recognition Using Graph Network with Pose Sequences. Proceedings of the Pattern Recognition and Computer Vision: 4th Chinese Conference (PRCV 2021), Beijing, China. Part III.","DOI":"10.1007\/978-3-030-88010-1_41"},{"key":"ref_171","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wang, Y., and Li, A. (2021, January 10\u201317). Cross-View Gait Recognition with Deep Universal Linear Embeddings. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Montreal, QC, Canada.","DOI":"10.1109\/CVPR46437.2021.00898"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.neucom.2021.08.054","article-title":"Sequential Convolutional Network for Behavioral Pattern Extraction in Gait Recognition","volume":"463","author":"Ding","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Chai, T., Mei, X., Li, A., and Wang, Y. (2021, January 19\u201322). Silhouette-Based View-Embeddings for Gait Recognition Under Multiple Views. Proceedings of the 2021 IEEE International Conference on Image Processing (ICIP), Anchorage, AK, USA.","DOI":"10.1109\/ICIP42928.2021.9506238"},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Zhu, H., Zheng, Z., and Nevatia, R. (2023, January 2\u20137). Gait Recognition Using 3-D Human Body Shape Inference. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV56688.2023.00097"},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"e12541","DOI":"10.1111\/exsy.12541","article-title":"A Multilevel Paradigm for Deep Convolutional Neural Network Features Selection with an Application to Human Gait Recognition","volume":"39","author":"Arshad","year":"2022","journal-title":"Expert Syst."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1080\/09540091.2022.2026294","article-title":"Multi-Stream Part-Fused Graph Convolutional Networks for Skeleton-Based Gait Recognition","volume":"34","author":"Wang","year":"2022","journal-title":"Connect. Sci."},{"key":"ref_177","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 Biometric Authentication: International ECCV 2002 Workshop, Copenhagen, Denmark.","DOI":"10.1007\/3-540-47917-1_16"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4875\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:37:53Z","timestamp":1760125073000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/10\/4875"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,18]]},"references-count":177,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23104875"],"URL":"https:\/\/doi.org\/10.3390\/s23104875","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,18]]}}}