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Báo cáo hóa học: " Research Article View Influence Analysis and Optimization for Multiview Face Recognition"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article View Influence Analysis and Optimization for Multiview Face Recognition | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2007 Article ID 25409 8 pages doi 10.1155 2007 25409 Research Article View Influence Analysis and Optimization for Multiview Face Recognition Won-Sook Lee1 and Kyung-Ah Sohn2 1 School of Information Technology and Engineering University of Ottawa Ottawa Canada K1N6N5 2 Computer Science Department Carnegie Mellon University Pittsburgh PA 15213-3891 USA Received 1 May 2006 Revised 20 December 2006 Accepted 24 June 2007 Recommended by Christophe Garcia We present a novel method to recognize a multiview face i.e. to recognize a face under different views through optimization of multiple single-view face recognitions. Many current face descriptors show quite satisfactory results to recognize identity of people with given limited view especially for the frontal view but the full view of the human head has not yet been recognizable with commercially acceptable accuracy. As there are various single-view recognition techniques already developed for very high success rate for instance MPEG-7 advanced face recognizer we propose a new paradigm to facilitate multiview face recognition not through a multiview face recognizer but through multiple single-view recognizers. To retrieve faces in any view from a registered descriptor we need to give corresponding view information to the descriptor. As the descriptor needs to provide any requested view in 3D space we refer to it as 3D information that it needs to contain. Our analysis in various angled views checks the extent of each view influence and it provides a way to recognize a face through optimized integration of single view descriptors covering the view plane of horizontal rotation from -90 to 90 and vertical rotation from -30 to 30 . The resulting face descriptor based on multiple representative views which is of compact size shows reasonable face recognition performance on any view. Hence our face descriptor contains quite enough 3D .