Abstract
In this article, a high performance face recognition system based on local binary pattern (LBP) using the probability distribution functions (PDFs) of pixels in different mutually independent color channels which are robust to frontal homogenous illumination and planer rotation is proposed. The illumination of faces is enhanced by using the state-of-the-art technique which is using discrete wavelet transform and singular value decomposition. After equalization, face images are segmented by using local successive mean quantization transform followed by skin color-based face detection system. Kullback–Leibler distance between the concatenated PDFs of a given face obtained by LBP and the concatenated PDFs of each face in the database is used as a metric in the recognition process. Various decision fusion techniques have been used in order to improve the recognition rate. The proposed system has been tested on the FERET, HP, and Bosphorus face databases. The proposed system is compared with conventional and the state-of-the-art techniques. The recognition rates obtained using FVF approach for FERET database is 99.78% compared with 79.60 and 68.80% for conventional gray-scale LBP and principle component analysis-based face recognition techniques, respectively.
Highlights
Face recognition has been one of the most interesting research topics for over the past half century
The results clearly indicate that this superiority is achieved by using probability distribution functions (PDFs)-based face recognition in different color channels backed by the data fusion techniques
These results are significant, when compared with the recognition rates achieved by conventional principle component analysis (PCA) and linear discriminant analysis (LDA) and the state-of-the-art techniques such as local binary pattern (LBP), NMF, and INMF-based face recognition system
Summary
Face recognition has been one of the most interesting research topics for over the past half century. Class discrimination values show that KLD provides enough separation between classes in different color channels in PDF-based face recognition. As reported in [6,8], the PDF-based face recognition system can be implemented in various color channels such as HSI and YCbCr color spaces in which the luminance and chrominance are separated from each other. These multi decisions can be combined later by using. Their face recognition system can be explained as follows: a histogram of the labeled image f1(x,y) can be defined as
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