{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:30:29Z","timestamp":1760059829832,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T00:00:00Z","timestamp":1752537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Brain Korea 21 Program for Leading Universities and Students (BK21 FOUR) Marine Designeering Education Research Group"},{"name":"Pukyong National University Industry-University Cooperation Foundation\u2019s 2024 Post-Doc"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>With the advancement of information technology, human activity recognition (HAR) has been widely applied in fields such as intelligent surveillance, health monitoring, and human\u2013computer interaction. As a crucial component of HAR, facial recognition plays a key role, especially in vision-based activity recognition. However, current facial recognition models on the market perform poorly in handling blurry images and dynamic scenarios, limiting their effectiveness in real-world HAR applications. This study aims to construct a fast and accurate facial recognition model based on novel adversarial learning and deblurring theory to enhance its performance in human activity recognition. The model employs a generative adversarial network (GAN) as the core algorithm, optimizing its generation and recognition modules by decomposing the global loss function and incorporating a feature pyramid, thereby solving the balance challenge in GAN training. Additionally, deblurring techniques are introduced to improve the model\u2019s ability to handle blurry and dynamic images. Experimental results show that the proposed model achieves high accuracy and recall rates across multiple facial recognition datasets, with an average recall rate of 87.40% and accuracy rates of 81.06% and 79.77% on the YTF, IMDB-WIKI, and WiderFace datasets, respectively. These findings confirm that the model effectively addresses the challenges of recognizing faces in dynamic and blurry conditions in human activity recognition, demonstrating significant application potential.<\/jats:p>","DOI":"10.3390\/jimaging11070241","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T09:45:52Z","timestamp":1752572752000},"page":"241","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Self-Supervised Adversarial Deblurring Face Recognition Network for Edge Devices"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2000-7523","authenticated-orcid":false,"given":"Hanwen","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea"}]},{"given":"Myun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea"}]},{"given":"Baitong","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Product Design, Tianjin Sino-German University of Applied Sciences, No. 1310, Dagu South Road, Hexi District, Tianjin 300220, China"}]},{"given":"Yanping","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1007\/s00170-021-07253-6","article-title":"Harnessing fuzzy neural network for gear fault diagnosis with limited data labels","volume":"115","author":"Zhou","year":"2021","journal-title":"Int. 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