{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T17:32:00Z","timestamp":1789234320832,"version":"build-2803163510"},"reference-count":76,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,19]],"date-time":"2025-07-19T00:00:00Z","timestamp":1752883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Begum Rokeya University, Rangpur"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Gait recognition is a reliable biometric approach that uniquely identifies individuals based on their natural walking patterns. It is widely used to recognize individuals who are challenging to camouflage and do not require a person\u2019s cooperation. The general face-based person recognition system often fails to determine the offender\u2019s identity when they conceal their face by wearing helmets and masks to evade identification. In such cases, gait-based recognition is ideal for identifying offenders, and most existing work leverages a deep learning (DL) model. However, a single model often fails to capture a comprehensive selection of refined patterns in input data when external factors are present, such as variation in viewing angle, clothing, and carrying conditions. In response to this, this paper introduces a fusion-based multi-model gait recognition framework that leverages the potential of convolutional neural networks (CNNs) and a vision transformer (ViT) in an ensemble manner to enhance gait recognition performance. Here, CNNs capture spatiotemporal features, and ViT features multiple attention layers that focus on a particular region of the gait image. The first step in this framework is to obtain the Gait Energy Image (GEI) by averaging a height-normalized gait silhouette sequence over a gait cycle, which can handle the left\u2013right gait symmetry of the gait. After that, the GEI image is fed through multiple pre-trained models and fine-tuned precisely to extract the depth spatiotemporal feature. Later, three separate fusion strategies are conducted, and the first one is decision-level fusion (DLF), which takes each model\u2019s decision and employs majority voting for the final decision. The second is feature-level fusion (FLF), which combines the features from individual models through pointwise addition before performing gait recognition. Finally, a hybrid fusion combines DLF and FLF for gait recognition. The performance of the multi-model fusion-based framework was evaluated on three publicly available gait databases: CASIA-B, OU-ISIR D, and the OU-ISIR Large Population dataset. The experimental results demonstrate that the fusion-enhanced framework achieves superior performance.<\/jats:p>","DOI":"10.3390\/sym17071155","type":"journal-article","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T07:56:14Z","timestamp":1753084574000},"page":"1155","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MMF-Gait: A Multi-Model Fusion-Enhanced Gait Recognition Framework Integrating Convolutional and Attention Networks"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8509-7957","authenticated-orcid":false,"given":"Kamrul","family":"Hasan","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Begum Rokeya University, Rangpur 5404, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khandokar Alisha","family":"Tuhin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Begum Rokeya University, Rangpur 5404, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5768-2309","authenticated-orcid":false,"given":"Md Rasul Islam","family":"Bapary","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Begum Rokeya University, Rangpur 5404, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6948-7768","authenticated-orcid":false,"given":"Md Shafi Ud","family":"Doula","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Begum Rokeya University, Rangpur 5404, Bangladesh"},{"name":"Department of Information and Communication Technologies, Asian Institute of Technology, Pathum Thani 12120, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7562-477X","authenticated-orcid":false,"given":"Md Ashraful","family":"Alam","sequence":"additional","affiliation":[{"name":"Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo 113-8655, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8355-7004","authenticated-orcid":false,"given":"Md Atiqur Rahman","family":"Ahad","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Digital Technologies, University of East London, London E16 2RD, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9334-4896","authenticated-orcid":false,"given":"Md. Zasim","family":"Uddin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Begum Rokeya University, Rangpur 5404, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jain, A., Bolle, R., and Pankanti, S. (1996). Introduction to Biometrics, Springer.","DOI":"10.1007\/b117227"},{"key":"ref_2","first-page":"157","article-title":"A review of person recognition based on face model","volume":"4","author":"Kak","year":"2018","journal-title":"Eurasian J. Sci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Besbes, F., Trichili, H., and Solaiman, B. (2008, January 7\u201311). Multimodal biometric system based on fingerprint identification and iris recognition. Proceedings of the 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications, Damascus, Syria.","DOI":"10.1109\/ICTTA.2008.4530129"},{"key":"ref_4","unstructured":"Bachoo, A.K., and Tapamo, J.R. (2004, January 15\u201317). A segmentation method to improve iris-based person identification. Proceedings of the 2004 IEEE Africon, 7th Africon Conference in Africa (IEEE Cat. No. 04CH37590), Gaborone, Botswana."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s41074-017-0029-0","article-title":"MultiQ: Single sensor-based multi-quality multi-modal large-scale biometric score database and its performance evaluation","volume":"9","author":"Uddin","year":"2017","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Makihara, Y., Nixon, M.S., and Yagi, Y. (2020). Gait recognition: Databases, representations, and applications. Computer Vision: A Reference Guide, Springer.","DOI":"10.1007\/978-3-030-63416-2_883"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1111\/j.1556-4029.2011.01793.x","article-title":"On using gait in forensic biometrics","volume":"56","author":"Bouchrika","year":"2011","journal-title":"J. Forensic Sci."},{"key":"ref_9","first-page":"1187","article-title":"Gait verification system for criminal investigation","volume":"8","author":"Iwama","year":"2013","journal-title":"Inf. Media Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1049\/iet-bmt.2013.0090","article-title":"Gait as evidence","volume":"3","author":"Lynnerup","year":"2014","journal-title":"IET Biom."},{"key":"ref_11","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_12","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_13","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1109\/TIP.2014.2371335","article-title":"Gait-based person recognition using arbitrary view transformation model","volume":"24","author":"Muramatsu","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s41074-019-0061-3","article-title":"Spatio-temporal silhouette sequence reconstruction for gait recognition against occlusion","volume":"11","author":"Uddin","year":"2019","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"158597","DOI":"10.1109\/ACCESS.2024.3482430","article-title":"Improving Gait Recognition through Occlusion Detection and Silhouette Sequence Reconstruction","volume":"12","author":"Hasan","year":"2024","journal-title":"IEEE Access"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Al-Jawad, N., and Sabir, A.T. (2014, January 13\u201317). Gait recognition based on Kinect sensor. Proceedings of the Real-Time Image and Video Processing 2014, SPIE, Brussels, Belgium.","DOI":"10.1117\/12.2052588"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3636","DOI":"10.1109\/TCSVT.2018.2883449","article-title":"Human gait recognition based on deterministic learning and data stream of microsoft kinect","volume":"29","author":"Deng","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_19","unstructured":"Sah, S., and Panday, S.P. (2020, January 28\u201330). Model based gait recognition using weighted KNN. Proceedings of the 8th IOE Graduate Conference, Okayama, Japan."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Lin, B., Zhang, S., and Yu, X. (2021, January 11\u201317). Gait recognition via effective global-local feature representation and local temporal aggregation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.01438"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Redmon, J. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_27","unstructured":"Simonyan, K. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_29","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_30","unstructured":"Vaswani, A. (2017). Attention is all you need. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_31","unstructured":"Dosovitskiy, A. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Mogan, J.N., Lee, C.P., Lim, K.M., and Muthu, K.S. (2022). VGG16-MLP: Gait recognition with fine-tuned VGG-16 and multilayer perceptron. Appl. Sci., 12.","DOI":"10.3390\/app12157639"},{"key":"ref_33","unstructured":"Pushpalatha, K., Neha, V., Prajwal, P., Ashraf, M., and Chiplunkar, C.H. (2023, January 13\u201314). ResNet-Based Gait Recognition: Leveraging Deep Learning for Accurate Biometric Identification. Proceedings of the 2023 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Mangalore, India."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1016\/S0167-8655(97)00062-7","article-title":"Combining evidence in personal identity verification systems","volume":"18","author":"Kittler","year":"1997","journal-title":"Pattern Recognit. Lett."},{"key":"ref_36","unstructured":"Ross, A.A., Nandakumar, K., and Jain, A.K. (2006). Handbook of Multibiometrics, Springer Science & Business Media."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"115396","DOI":"10.1016\/j.measurement.2024.115396","article-title":"Human Gait phases recognition based on multi-source data fusion and BILSTM attention neural network","volume":"238","author":"Zhan","year":"2024","journal-title":"Measurement"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"13676","DOI":"10.1109\/JSEN.2024.3373100","article-title":"Gait recognition based on two-stream CNNs with multisensor progressive feature fusion","volume":"24","author":"Qin","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"9777","DOI":"10.1109\/TNNLS.2025.3526815","article-title":"Rethinking Appearance-Based Deep Gait Recognition: Reviews, Analysis, and Insights From Gait Recognition Evolution","volume":"36","author":"Li","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1504\/IJICA.2021.113618","article-title":"Person identification using fusion of deep net facial features","volume":"12","author":"Mehraj","year":"2021","journal-title":"Int. J. Innov. Comput. Appl."},{"key":"ref_41","unstructured":"Makihara, Y., Sagawa, R., Mukaigawa, Y., Echigo, T., and Yagi, Y. (2006, January 17\u201322). Which reference view is effective for gait identification using a view transformation model?. Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW\u201906), New York, NY, USA."},{"key":"ref_42","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_43","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), Hong Kong, China."},{"key":"ref_44","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_45","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_46","unstructured":"Bouchrika, I., and Nixon, M.S. (2007, January 28\u201330). Model-based feature extraction for gait analysis and recognition. Proceedings of the Computer Vision\/Computer Graphics Collaboration Techniques: Third International Conference, MIRAGE 2007, Rocquencourt, France. Proceedings 3."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yoo, J.H., Hwang, D., Moon, K.Y., and Nixon, M.S. (2008, January 23\u201326). Automated human recognition by gait using neural network. Proceedings of the 2008 First Workshops on Image Processing Theory, Tools and Applications, Sousse, Tunisia.","DOI":"10.1109\/IPTA.2008.4743792"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"259","DOI":"10.4218\/etrij.11.1510.0068","article-title":"Automated markerless analysis of human gait motion for recognition and classification","volume":"33","author":"Yoo","year":"2011","journal-title":"Etri J."},{"key":"ref_49","unstructured":"Preis, J., Kessel, M., Werner, M., and Linnhoff-Popien, C. (2012, January 18). Gait recognition with kinect. Proceedings of the 1st International Workshop on Kinect in Pervasive Computing, New Castle, UK."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"162708","DOI":"10.1109\/ACCESS.2019.2952065","article-title":"Artificial neural network based gait recognition using kinect sensor","volume":"7","author":"Bari","year":"2019","journal-title":"IEEE Access"},{"key":"ref_51","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_52","doi-asserted-by":"crossref","unstructured":"Jiang, B., Zhang, Z., Lin, D., Tang, J., and Luo, B. (2019, January 15\u201320). Semi-supervised learning with graph learning-convolutional networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01157"},{"key":"ref_53","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_54","doi-asserted-by":"crossref","unstructured":"Teepe, T., Gilg, J., Herzog, F., H\u00f6rmann, S., and Rigoll, G. (2021, January 19\u201322). Towards a Deeper Understanding of Skeleton-based Gait Recognition, 2022. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Anchorage, AK, USA.","DOI":"10.1109\/CVPRW56347.2022.00163"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3455","DOI":"10.1007\/s00371-023-02973-0","article-title":"ResGait: Gait feature refinement based on residual structure for gait recognition","volume":"39","author":"Gao","year":"2023","journal-title":"Vis. Comput."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ray, A., Uddin, M.Z., Hasan, K., Melody, Z.R., Sarker, P.K., and Ahad, M.A.R. (2024). Multi-Biometric Feature Extraction from Multiple Pose Estimation Algorithms for Cross-View Gait Recognition. Sensors, 24.","DOI":"10.3390\/s24237669"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Song, Y.F., Zhang, Z., Shan, C., and Wang, L. (2020, January 12\u201316). Stronger, faster and more explainable: A graph convolutional baseline for skeleton-based action recognition. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA.","DOI":"10.1145\/3394171.3413802"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1109\/TPAMI.2006.38","article-title":"Individual recognition using gait energy image","volume":"28","author":"Han","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Liu, J., and Zheng, N. (2007, January 2\u20135). Gait history image: A novel temporal template for gait recognition. Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, Beijing, China.","DOI":"10.1109\/ICME.2007.4284737"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Bashir, K., Xiang, T., and Gong, S. (2009, January 3). Gait recognition using gait entropy image. Proceedings of the 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009), IET, London, UK.","DOI":"10.1049\/ic.2009.0230"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhang, J., Pu, J., Yuan, X., and Wang, L. (2010, January 5\u201311). Chrono-gait image: A novel temporal template for gait recognition. Proceedings of the Computer Vision\u2013ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece. Proceedings, Part I 11.","DOI":"10.1007\/978-3-642-15549-9_19"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1016\/j.patcog.2010.10.011","article-title":"Gait flow image: A silhouette-based gait representation for human identification","volume":"44","author":"Lam","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_63","first-page":"604","article-title":"Deep large margin nearest neighbor for gait recognition","volume":"30","author":"Xu","year":"2021","journal-title":"J. Intell. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Junaid, I., and Ari, S. (2022, January 11\u201315). Gait recognition under different covariate conditions using deep learning technique. Proceedings of the 2022 IEEE International Conference on Signal Processing and Communications (SPCOM), Bangalore, India.","DOI":"10.1109\/SPCOM55316.2022.9840818"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Suthar, O., Katkar, V., and Vaghela, K. (, January 10\u201311). Person Recognition using Gait Energy Image, MobileNetV3Small and Machine Learning. Proceedings of the 2023 IEEE 3rd International Conference on Technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), Mysuru, India.","DOI":"10.1109\/TEMSMET56707.2023.10150084"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"110090","DOI":"10.1016\/j.patcog.2023.110090","article-title":"Gait feature learning via spatio-temporal two-branch networks","volume":"147","author":"Chen","year":"2024","journal-title":"Pattern Recognit."},{"key":"ref_67","first-page":"185511","article-title":"Horizontal and Vertical Part-wise Feature Extraction for Coss-view Gait Recognition","volume":"12","author":"Uddin","year":"2024","journal-title":"IEEE Access"},{"key":"ref_68","first-page":"78","article-title":"A review of gait cycle and its parameters","volume":"13","author":"Kharb","year":"2011","journal-title":"IJCEM Int. J. Comput. Eng. Manag."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"385","DOI":"10.62411\/faith.3048-3719-62","article-title":"High-Performance Face Spoofing Detection using Feature Fusion of FaceNet and Tuned DenseNet201","volume":"1","author":"Zuama","year":"2025","journal-title":"J. Future Artif. Intell. Technol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"107185","DOI":"10.1016\/j.engappai.2023.107185","article-title":"Deep learning with image-based autism spectrum disorder analysis: A systematic review","volume":"127","author":"Uddin","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_72","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_73","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_74","doi-asserted-by":"crossref","unstructured":"Min, P.P., Sayeed, S., and Ong, T.S. (2019, January 24\u201326). Gait recognition using deep convolutional features. Proceedings of the 2019 7th International Conference on Information and Communication Technology (ICoICT), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICoICT.2019.8835194"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Aung, H.M.L., and Pluempitiwiriyawej, C. (2020, January 4\u20136). Gait biometric-based human recognition system using deep convolutional neural network in surveillance system. Proceedings of the 2020 Asia Conference on Computers and Communications (ACCC), Singapore.","DOI":"10.1109\/ACCC51160.2020.9347899"},{"key":"ref_76","first-page":"472","article-title":"Deep features based Multiview gait recognition","volume":"12","author":"Balamurugan","year":"2021","journal-title":"Turk. J. Comput. Math. Educ. (TURCOMAT)"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/7\/1155\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:12:30Z","timestamp":1760033550000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/7\/1155"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,19]]},"references-count":76,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["sym17071155"],"URL":"https:\/\/doi.org\/10.3390\/sym17071155","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,19]]}}}