{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:07:16Z","timestamp":1760144836942,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T00:00:00Z","timestamp":1716249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Double First-Class Innovation Research Project for the People\u2019s Public Security University of China","award":["2023SYL08"],"award-info":[{"award-number":["2023SYL08"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>To address challenges related to the inadequate representation and inaccurate discrimination of pedestrian attributes, we propose a novel method for person re-identification, which leverages global feature learning and classification optimization. Specifically, this approach integrates a Normalization-based Channel Attention Module into the fundamental ResNet50 backbone, utilizing a scaling factor to prioritize and enhance key pedestrian feature information. Furthermore, dynamic activation functions are employed to adaptively modulate the parameters of ReLU based on the input convolutional feature maps, thereby bolstering the nonlinear expression capabilities of the network model. By incorporating Arcface loss into the cross-entropy loss, the supervised model is trained to learn pedestrian features that exhibit significant inter-class variance while maintaining tight intra-class coherence. The evaluation of the enhanced model on two popular datasets, Market1501 and DukeMTMC-ReID, reveals improvements in Rank-1 accuracy by 1.28% and 1.4%, respectively, along with corresponding gains in the mean average precision (mAP) of 1.93% and 1.84%. These findings indicate that the proposed model is capable of extracting more robust pedestrian features, enhancing feature discriminability, and ultimately achieving superior recognition accuracy.<\/jats:p>","DOI":"10.3390\/e26060436","type":"journal-article","created":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T07:56:11Z","timestamp":1716364571000},"page":"436","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing Person Re-Identification through Attention-Driven Global Features and Angular Loss Optimization"],"prefix":"10.3390","volume":"26","author":[{"given":"Yihan","family":"Bi","sequence":"first","affiliation":[{"name":"School of Information and Cyber Security, People\u2019s Public Security University of China, Beijing 100038, China"}]},{"given":"Rong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Cyber Security, People\u2019s Public Security University of China, Beijing 100038, China"},{"name":"Key Laboratory of Security Prevention Technology and Risk Assessment of Ministry of Public Security, Beijing 100038, China"}]},{"given":"Qianli","family":"Zhou","sequence":"additional","affiliation":[{"name":"Beijing Public Security Bureau, Beijing 100038, China"}]},{"given":"Ronghui","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Information and Cyber Security, People\u2019s Public Security University of China, Beijing 100038, China"}]},{"given":"Mingjie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Cyber Security, People\u2019s Public Security University of China, Beijing 100038, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hu, M., Zeng, K., Wang, Y., and Guo, Y. 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