{"title":"Attention-aware ensemble learning for face-periocular cross-modality matching","authors":"Tiong-Sik Ng, Andrew Beng Jin Teoh","doi":"10.1016/j.asoc.2025.113044","DOIUrl":null,"url":null,"abstract":"<div><div>Face and periocular regions serve as complementary biometric modalities in identity recognition. The face-periocular cross-modality matching (FPCM) provides a versatile solution, especially when traditional face recognition systems encounter challenges due to occlusions or the presence of sunglasses, which can obscure the periocular region, rendering it less effective in periocular recognition systems. This paper introduces a novel approach based on attention-aware ensemble learning (AEL) to address these challenges. This notion is embodied in AELNet, which features an attention-aware shared-parameter encoder and multiple classifier heads. AELNet is designed to harness the complementary features of the face and periocular regions, enhancing the quality of joint embeddings. A key aspect of AELNet is its ability to foster diversity among the classifier heads through unique embedding techniques and batch sampling strategies, ultimately boosting FPCM performance. We demonstrate the effectiveness of the AELNet by conducting extensive experiments on five unconstrained periocular-face datasets as a benchmark. Codes are publicly available at <span><span>https://github.com/tiongsikng/ael_net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"175 ","pages":"Article 113044"},"PeriodicalIF":7.2000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Soft Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1568494625003552","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Face and periocular regions serve as complementary biometric modalities in identity recognition. The face-periocular cross-modality matching (FPCM) provides a versatile solution, especially when traditional face recognition systems encounter challenges due to occlusions or the presence of sunglasses, which can obscure the periocular region, rendering it less effective in periocular recognition systems. This paper introduces a novel approach based on attention-aware ensemble learning (AEL) to address these challenges. This notion is embodied in AELNet, which features an attention-aware shared-parameter encoder and multiple classifier heads. AELNet is designed to harness the complementary features of the face and periocular regions, enhancing the quality of joint embeddings. A key aspect of AELNet is its ability to foster diversity among the classifier heads through unique embedding techniques and batch sampling strategies, ultimately boosting FPCM performance. We demonstrate the effectiveness of the AELNet by conducting extensive experiments on five unconstrained periocular-face datasets as a benchmark. Codes are publicly available at https://github.com/tiongsikng/ael_net.
期刊介绍:
Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities.
Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.