Full metadata record
DC FieldValueLanguage
dc.contributor.authorHsu, Heng-Weien_US
dc.contributor.authorWu, Tung-Yuen_US
dc.contributor.authorWong, Wing Hungen_US
dc.contributor.authorLee, Chen-Yien_US
dc.date.accessioned2019-04-02T06:04:14Z-
dc.date.available2019-04-02T06:04:14Z-
dc.date.issued2018-01-01en_US
dc.identifier.urihttp://hdl.handle.net/11536/150764-
dc.description.abstractFinding the locations and identities of faces in videos is a very important task in numerous applications. In this paper, we propose a correlation-based face detection approach to improve the performance of face recognition tasks for videos. We apply correlation measures to pairs of response maps which are generated from automatically selected neurons in deep convolutional neural network (CNN) models to detect faces in each video frame. The embeddings extracted from faces cropped by our proposed approach are more consistent across each video sequence and more suitable for face recognition and clustering tasks. Experimental results from the YouTube Faces (YTF) dataset demonstrate that our proposed approach is more robust and achieves better recognition accuracy compared to state-of-the-art face detection approaches.en_US
dc.language.isoen_USen_US
dc.subjectConvolutional neural networken_US
dc.subjectdeep learningen_US
dc.subjectneuron selectionen_US
dc.subjectface detectionen_US
dc.subjectface recognitionen_US
dc.titleCORRELATION-BASED FACE DETECTION FOR RECOGNIZING FACES IN VIDEOSen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)en_US
dc.citation.spage3101en_US
dc.citation.epage3105en_US
dc.contributor.department電子工程學系及電子研究所zh_TW
dc.contributor.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.identifier.wosnumberWOS:000446384603054en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper