{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:17:30Z","timestamp":1780355850733,"version":"3.54.1"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T00:00:00Z","timestamp":1736726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jilin Provincial Education Science Planning Project","award":["GH23198"],"award-info":[{"award-number":["GH23198"]}]},{"name":"Jilin Provincial Education Science Planning Project","award":["CXTD2023005"],"award-info":[{"award-number":["CXTD2023005"]}]},{"name":"Research and Innovation Team Project of Zhongshan Science and Technology Bureau","award":["GH23198"],"award-info":[{"award-number":["GH23198"]}]},{"name":"Research and Innovation Team Project of Zhongshan Science and Technology Bureau","award":["CXTD2023005"],"award-info":[{"award-number":["CXTD2023005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>For surveillance video management in university laboratories, issues such as occlusion and low-resolution face capture often arise. Traditional face recognition algorithms are typically static and rely heavily on clear images, resulting in inaccurate recognition for low-resolution, small-sized faces. To address the challenges of occlusion and low-resolution person identification, this paper proposes a new face recognition framework by reconstructing Retinaface-Resnet and combining it with Quality-Adaptive Margin (adaface). Currently, although there are many target detection algorithms, they all require a large amount of data for training. However, datasets for low-resolution face detection are scarce, leading to poor detection performance of the models. This paper aims to solve Retinaface\u2019s weak face recognition capability in low-resolution scenarios and its potential inaccuracies in face bounding box localization when faces are at extreme angles or partially occluded. To this end, Spatial Depth-wise Separable Convolutions are introduced. Retinaface-Resnet is designed for face detection and localization, while adaface is employed to address low-resolution face recognition by using feature norm approximation to estimate image quality and applying an adaptive margin function. Additionally, a multi-object tracking algorithm is used to solve the problem of moving occlusion. Experimental results demonstrate significant improvements, achieving an accuracy of 96.12% on the WiderFace dataset and a recognition accuracy of 84.36% in practical laboratory applications.<\/jats:p>","DOI":"10.3390\/jimaging11010024","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T09:49:17Z","timestamp":1736761757000},"page":"24","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["LittleFaceNet: A Small-Sized Face Recognition Method Based on RetinaFace and AdaFace"],"prefix":"10.3390","volume":"11","author":[{"given":"Zhengwei","family":"Ren","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130012, China"},{"name":"Zhongshan Institute of Changchun University of Science and Technology, Zhongshan 528400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130012, China"},{"name":"Zhongshan Institute of Changchun University of Science and Technology, Zhongshan 528400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongsheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130012, China"},{"name":"Zhongshan Institute of Changchun University of Science and Technology, Zhongshan 528400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130012, China"},{"name":"Zhongshan Institute of Changchun University of Science and Technology, Zhongshan 528400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,13]]},"reference":[{"key":"ref_1","first-page":"17","article-title":"Application of deep learning face recognition technology in attendance system","volume":"10","author":"Liu","year":"2020","journal-title":"Intell. Comput. Appl."},{"key":"ref_2","first-page":"72","article-title":"Occlusion face recognition based on improved GD-HASLR algorithm","volume":"36","author":"Xu","year":"2023","journal-title":"Electron. Sci. Technol."},{"key":"ref_3","unstructured":"Zhang, K. (2018). Research on Face Recognition Algorithm Under Unconstrained Conditions Based on Deep Learning. [Master\u2019s Thesis, China Jiliang University]."},{"key":"ref_4","unstructured":"Ni, K. (2020). Research on Face Recognition Under Different Lighting Conditions Based on Deep Learning. [Master\u2019s Thesis, Beijing University of Posts and Telecommunications]."},{"key":"ref_5","first-page":"398","article-title":"Face recognition based on multi-task cascaded CNN and center loss","volume":"37","author":"Wang","year":"2020","journal-title":"Comput. Simul."},{"key":"ref_6","unstructured":"Wu, D. (2020). Multi-Face Detection and Recognition in Natural Scenes Based on Deep Learning. [Master\u2019s Thesis, Shanxi University]."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Ververas, E., Kotsia, I., and Zafeiriou, S. (2020, January 14\u201319). RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Yang, J., Xue, N., Kotsia, I., and Zafeiriou, S. (2019, January 16\u201320). ArcFace: Additive Angular Margin Loss for Deep Face Recognition. Proceedings of the CVPR 2019, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00482"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Yang, J., Lattas, A., and Zafeiriou, S. (2021, January 19\u201325). Variational Prototype Learning for Deep Face Recognition. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01173"},{"key":"ref_11","unstructured":"Huang, G.B., Ramesh, M., Berg, T., and Learned-Miller, E. (2008, January 17). Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. Proceedings of the ECCV 2008, Marseille, France."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sengupta, S., Chen, J.-C., Castillo, C., Patel, V.M., Chellappa, R., and Jacobs, D.W. (2016, January 7\u201310). Frontal to profile face verification in the wild. Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA.","DOI":"10.1109\/WACV.2016.7477558"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kalka, N.D., Maze, B., Duncan, J.A., O\u2019Connor, K., Elliott, S., Hebert, K., Bryan, J., and Jain, K.A. (2018, January 17\u201320). IJB\u2013S: IARPA Janus Surveillance Video Benchmark. Proceedings of the 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), Redondo Beach, CA, USA.","DOI":"10.1109\/BTAS.2018.8698584"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Whitelam, C., Taborsky, E., Blanton, A., Maze, B., Adams, J., Miller, T., Kalka, N., Jain, A.K., Duncan, J.A., and Allen, K. (2017, January 21\u201326). IARPA Janus Benchmark-B Face Dataset. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.87"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guo, Y., Zhang, L., Hu, Y., He, X., and Gao, J. (2016, January 8\u201316). MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition. Proceedings of the Computer Vision\u2014ECCV 2016, Lecture Notes in Computer Science, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46487-9_6"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tran, L., Yin, X., and Liu, X. (2017, January 21\u201326). Disentangled Representation Learning GAN for Pose-Invariant Face Recognition. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.141"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ju, Y.-J., Lee, G.-H., Hong, J.-H., and Lee, S.-W. (2022, January 4\u20138). Complete Face Recovery GAN: Unsupervised Joint Face Rotation and De-Occlusion from a Single-View Image. Proceedings of the WACV 2022, Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00124"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Abbasian, M., Rajabzadeh, T., Moradipari, A., Aqajari, S.A.H., Lu, H., and Rahmani, A. (2024, January 8\u201312). Controlling the Latent Space of GANs through Reinforcement Learning: A Case Study on Task-based Image-to-Image Translation. Proceedings of the 39th ACM\/SIGAPP Symposium on Applied Computing, Avignon, France.","DOI":"10.1145\/3605098.3636158"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, Y., Gong, D., Zhou, Z., Ji, X., Wang, H., Li, Z., Liu, W., and Zhang, T. (2018, January 8\u201314). Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition. Proceedings of the Computer Vision\u2014ECCV 2018, Munich, Germany. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-01267-0_45"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shin, S., Lee, J., Lee, J., Yu, Y., and Lee, K. (2022, January 23\u201327). Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition. Proceedings of the ECCV 2022, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19775-8_37"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kim, M., Jain, A., and Liu, X. (2022, January 19\u201324). AdaFace: Quality Adaptive Margin for Face Recognition. Proceedings of the CVPR 2022, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01819"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Herdade, S., Thadani, K., Dodds, E., Culpepper, J., and Ku, Y.-N. (2023, January 3\u20137). Unifying Margin-Based Softmax Losses in Face Recognition. Proceedings of the WACV 2023, Waikoloa, Hawaii, USA.","DOI":"10.1109\/WACV56688.2023.00354"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, H., Zhu, X., Lei, Z., and Li, S.Z. (2019, January 16\u201320). AdaptiveFace: Adaptive Margin and Sampling for Face Recognition. Proceedings of the CVPR 2019, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01222"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Meng, Q., Zhao, S., Huang, Z., and Zhou, F. (2021, January 19\u201325). MagFace: A Universal Representation for Face Recognition and Quality Assessment. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01400"},{"key":"ref_25","unstructured":"Saadabadi, M.S.E., Malakshan, S.R., Zafari, A., Mostofa, M., and Nasrabadi, N.M. (2023, January 3\u20137). Quality Aware Sample-to-Sample Comparison for Face Recognition. Proceedings of the WACV 2023, Waikoloa, HI, USA."},{"key":"ref_26","unstructured":"Nourelahi, M., Kotthoff, L., Chen, P., and Nguyen, A. (2022). How explainable are adversarially-robust CNNs?. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.cviu.2013.11.009","article-title":"Robust PCA via principal component pursuit: A review for a comparative evaluation in video surveillance","volume":"122","author":"Bouwmans","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1007\/s00138-013-0535-8","article-title":"Context-based person identification framework for smart video surveillance","volume":"25","author":"Zhang","year":"2014","journal-title":"Mach. Vis. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1109\/TITS.2016.2582900","article-title":"Towards detection of bus driver fatigue based on robust visual analysis of eye state","volume":"18","author":"Mandal","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TMM.2017.2751966","article-title":"PROVID: Progressive and multimodal vehicle reidentification for large-scale urban surveillance","volume":"20","author":"Liu","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1109\/TPAMI.2017.2700390","article-title":"Trunk-branch ensemble convolutional neural networks for video-based face recognition","volume":"40","author":"Ding","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","unstructured":"Wang, Y., Bao, T., Ding, C., and Zhu, M. (2017, January 2\u20134). Face recognition in real-world surveillance videos with deep learning method. Proceedings of the 2017 2nd International Conference on Image, Vision and Computing (ICIVC), Chengdu, China."},{"key":"ref_33","first-page":"79","article-title":"Face recognition-based real-time system for surveillance","volume":"11","author":"Mahdi","year":"2017","journal-title":"Intell. Decis. Technol."},{"key":"ref_34","unstructured":"Deng, S. (2018). Research and Implementation of Security Management System Based on Face Recognition. [Master\u2019s Thesis, Liaoning University]."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jose, E., Manikandan, G., Haridas, M.T.P., and Supriya, M. (2019, January 15\u201316). Face recognition based surveillance system using facenet and mtcnn on jetson tx2. Proceedings of the 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Coimbaore, India.","DOI":"10.1109\/ICACCS.2019.8728466"},{"key":"ref_36","unstructured":"Wang, K. (2021). Design and Research of Face Recognition Laboratory Access Control Management System Based on Temperature Liveness Detection. [Master\u2019s Thesis, Shandong Jiaotong University]."},{"key":"ref_37","unstructured":"Dong, Z. (2023). Research on Face Recognition Algorithm Based on Super-Resolution Reconstruction in Smart Classroom Management System. [Master\u2019s Thesis, Guizhou Normal University]."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ylioinas, J., Kannala, J., Hadid, A., and Pietikainen, M. (2006). Face Recognition Using Smoothed High-Dimensional Representation. Image Analysis, Proceedings of the SCIA 2015, Copenhagen, Denmark, 15\u201317 June 2015, CRC Press. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-19665-7_44"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Najibi, M., Samangouei, P., Chellappa, R., and Davis, L. (2017, January 22\u201329). SSH: Single Stage Headless Face Detector. Proceedings of the ICCV 2017, Venice, Italy.","DOI":"10.1109\/ICCV.2017.522"},{"key":"ref_40","unstructured":"Loy, C.C., Lin, D., Ouyang, W., Xiong, Y., Yang, S., Huang, Q., Zhou, D., Xia, W., Li, Q., and Luo, P. (2019). Wider face and pedestrian challenge 2018: Methods and results. arXiv."},{"key":"ref_41","unstructured":"Amini, M.R., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., and Tsoumakas, G. (2022, January 19\u201323). No More Strided Convolutions or Pooling: A New CNN Building Block for Low-Resolution Images and Small Objects. Machine Learning and Knowledge Discovery in Databases, Proceedings of the ECML PKDD 2022, Grenoble, France. Lecture Notes in Computer Science."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., and Song, L. (2017, January 21\u201326). SphereFace: Deep Hypersphere Embedding for Face Recognition. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.713"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Huang, G., Deng, J., Ye, Y., Huang, J., Chen, X., Zhu, J., Yang, T., Lu, J., and Du, D. (2021, January 19\u201325). WebFace260M: A benchmark unveiling the power of million-scale deep face recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01035"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/1\/24\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:27:59Z","timestamp":1759919279000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/1\/24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,13]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["jimaging11010024"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11010024","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,13]]}}}