{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T07:43:09Z","timestamp":1748590989600},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:p>To address the challenges posed by unknown occlusions, we propose a Biased Feature Learning (BFL) framework for occlusion-invariant face recognition. We first construct an extended dataset using a multi-scale data augmentation method. For model training, we modify the label loss to adjust the impact of normal and occluded samples. Further, we propose a biased guidance strategy to manipulate the optimization of a network so that the feature embedding space is dominated by non-occluded faces. BFL not only enhances the robustness of a network to unknown occlusions but also maintains or even improves its performance for normal faces. Experimental results demonstrate its superiority as well as the generalization capability with different network architectures and loss functions.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/93","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T08:12:10Z","timestamp":1594195930000},"page":"666-672","source":"Crossref","is-referenced-by-count":6,"title":["Biased Feature Learning for Occlusion Invariant Face Recognition"],"prefix":"10.24963","author":[{"given":"Changbin","family":"Shao","sequence":"first","affiliation":[{"name":"Nanjing University"},{"name":"Jiangsu University of Science and Technology"}]},{"given":"Jing","family":"Huo","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Lei","family":"Qi","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Zhen-Hua","family":"Feng","sequence":"additional","affiliation":[{"name":"University of Surrey"}]},{"given":"Wenbin","family":"Li","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Chuanqi","family":"Dong","sequence":"additional","affiliation":[{"name":"Nanjing University"}]},{"given":"Yang","family":"Gao","sequence":"additional","affiliation":[{"name":"Nanjing University"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-PRICAI-2020","name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","start":{"date-parts":[[2020,7,11]]},"theme":"Artificial Intelligence","location":"Yokohama, Japan","end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T22:13:13Z","timestamp":1594246393000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/93"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/93","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}