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: Information Theory for Gabor Feature Selection for Face Recognition | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article 1D30274 Pages 1-11 DOI ASP 2006 30274 Information Theory for Gabor Feature Selection for Face Recognition Linlin Shen and Li Bai School of Computer Science and Information Technology The University of Nottingham Nottingham NG8 1BB UK Received 21 June 2005 Revised 23 September 2005 Accepted 26 September 2005 Recommended for Publication by Mark Liao A discriminative and robust feature kernel enhanced informative Gabor feature is proposed in this paper for face recognition. Mutual information is applied to select a set of informative and nonredundant Gabor features which are then further enhanced by kernel methods for recognition. Compared with one of the top performing methods in the 2004 Face Verification Competition FVC2004 our methods demonstrate a clear advantage over existing methods in accuracy computation efficiency and memory cost. The proposed method has been fully tested on the FERET database using the FERET evaluation protocol. Significant improvements on three of the test data sets are observed. Compared with the classical Gabor wavelet-based approaches using a huge number of features our method requires less than 4 milliseconds to retrieve a few hundreds of features. Due to the substantially reduced feature dimension only 4 seconds are required to recognize 200 face images. The paper also unified different Gabor filter definitions and proposed a training sample generation algorithm to reduce the effects caused by unbalanced number of samples available in different classes. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Daugman 1 presented evidence that visual neurons could optimize the general uncertainty relations for resolution in space spatial frequency and orientation. Gabor filters are believed to function similarly to the visual neurons of the human visual system. From an information-theoretic viewpoint .
