Abstract
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.
Highlights
The earliest work in computer recognition of faces was reported by Bledsoe [1], where manually located feature points are used
Each result is the average of 100 runs, where we have randomly shuffled the faces in each class
The experimental results have been achieved by testing the system on the following face databases: The Head Pose (HP) face database containing 150 faces of 15 classes with 10 different rotational poses varying from −90◦ to +90◦ for each class, a subset of the FERET face database containing 500 faces of 50 classes with 10 different poses varying from −90◦ to +90◦ for each class, the Essex University face database containing 1500 faces of 150 classes with 10 different slightly varying poses and illumination changes, and the Georgia Tech University face database containing 500 faces of 50 classes with 10 different varying poses, illumination, and background
Summary
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- (PCA-) based eigenfaces introduced by Turk and Pentland [2] attracted a lot of attention. Belhumeur et al [3] introduced the fisherfaces method which is based on linear discriminant analysis (LDA). Many of these methods are based on greyscale images; colour images are increasingly being used since they add additional biometric information for face recognition [4]. Images with small changes in translation, rotation, and illumination still possess high correlation in their corresponding PDFs, which prompts the idea of using PDFs for face recognition
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