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Báo cáo hóa học: " A Probabilistic Fusion Methodology for 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: A Probabilistic Fusion Methodology for Face Recognition | EURASIP Journal on Applied Signal Processing 2005 17 2772-2787 2005 Hindawi Publishing Corporation A Probabilistic Fusion Methodology for Face Recognition K. Srinivasa Rao Image Processing and Computer Vision Laboratory Department of Electrical Engineering Indian Institute of Technology Madras Chennai-600 036 India Email srinuj.itm2003@yahoo.co.in A. N. Rajagopalan Image Processing and Computer Vision Laboratory Department of Electrical Engineering Indian Institute of Technology Madras Chennai-600 036 India Email raju@ee.iitm.ernet.in Received 11 May 2004 Revised 30 March 2005 Recommended for Publication by Satya Dharanipragada We propose a novel probabilistic framework that combines information acquired from different facial features for robust face recognition. The features used are the entire face the edginess image of the face and the eyes. In the training stage individual feature spaces are constructed using principal component analysis PCA and Fisher s linear discriminant FLD . By using the distance-in-feature-space DIFS values of the training images the distributions of the DIFS values in each feature space are computed. For a given image the distributions of the DIFS values yield confidence weights for the three facial features extracted from the image. The final score is computed using a probabilistic fusion criterion and the match with the highest score is used to establish the identity of a person. A new preprocessing scheme for illumination compensation is also advocated. The proposed fusion approach is more reliable than a recognition system which uses only one feature trained individually. The method is validated on different face datasets including the FERET database. Keywords and phrases face recognition block histogram modification edginess image probabilistic fusion distance in feature space. 1. INTRODUCTION Automatic face recognition is a challenging problem in computer vision. Computers that recognize faces can be applied to a wide variety of .