báo cáo hóa học:" Research Article Data Fusion Boosted Face Recognition Based on Probability Distribution Functions in Different Colour Channels"

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 Data Fusion Boosted Face Recognition Based on Probability Distribution Functions in Different Colour Channels | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 482585 10 pages doi 2009 482585 Research Article Data Fusion Boosted Face Recognition Based on Probability Distribution Functions in Different Colour Channels Hasan Demirel EURASIP Member and Gholamreza Anbarjafari Department of Electrical and Electronic Engineering Eastern Mediterranean University Gazimagusa KKTC 10Mersin Turkey Correspondence should be addressed to Hasan Demirel Received 20 November 2008 Revised 9 April 2009 Accepted 20 May 2009 Recommended by Satya Dharanipragada A new and high performance face recognition system based on combining the decision obtained from the probability distribution functions PDFs of pixels in different colour channels is proposed. The PDFs of the equalized and segmented face images are used as statistical feature vectors for the recognition of faces by minimizing the Kullback-Leibler Divergence KLD between the PDF of a given face and the PDFs of faces in the database. Many data fusion techniques such as median rule sum rule max rule product rule and majority voting and also feature vector fusion as a source fusion technique have been employed to improve the recognition performance. The proposed system has been tested on the FERET the Head Pose the Essex University and the Georgia Tech University face databases. The superiority of the proposed system has been shown by comparing it with the state-of-art face recognition systems. Copyright 2009 H. Demirel and G. Anbarjafari. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. Introduction The earliest work in computer recognition of faces was reported by Bledsoe 1 where manually located feature points are used. Statistical face recognition systems such as principal component analysis-

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