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dc.contributor.authorChang, Pei-Chunen_US
dc.contributor.authorChen, Yong-Shengen_US
dc.contributor.authorLee, Chang-Hsingen_US
dc.contributor.authorLien, Cheng-Changen_US
dc.contributor.authorHan, Chin-Chuanen_US
dc.date.accessioned2019-04-02T06:04:17Z-
dc.date.available2019-04-02T06:04:17Z-
dc.date.issued2018-01-01en_US
dc.identifier.urihttp://hdl.handle.net/11536/150775-
dc.description.abstractRobust face recognition under illumination variations is a critical problem in a face recognition system, particularly for face recognition in the wild. In this paper, a face image preprocessing approach, called spatial expansion local histogram equalization (SELHE), is proposed to enhance face images due to illumination variations. First, a face image is divided into several non-overlapped blocks. Then, local histogram equalization with spatial expansion is proposed to enhance the contrast of each local image block. Local linear regression classification will then be used to recognize the enhanced image blocks. Experiments performed on the Yale B and Yale B extended databases have shown that the proposed approach yields promising recognition accuracy.en_US
dc.language.isoen_USen_US
dc.subjectface recognitionen_US
dc.subjecthistogram equalizationen_US
dc.subjectlinear regression classificationen_US
dc.titleIllumination Robust Face Recognition Using Spatial Expansion Local Histogram Equalization and Locally Linear Regression Classificationen_US
dc.typeProceedings Paperen_US
dc.identifier.journalPROCEEDINGS OF 2018 3RD INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION SYSTEMS (ICCCS)en_US
dc.citation.spage249en_US
dc.citation.epage253en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000446963700051en_US
dc.citation.woscount0en_US
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