Color Space Projection, Feature Fusion and Concurrent Neural Modules for Biometric Image Recognition
نویسنده
چکیده
– A new technique of color face recognition is proposed. First processing stage consists of an optimum color conversion from the 3D RGB space into a 2D selected feature space using the old Karhunen-Loève transform (KLT). The resulted 2D color space is defined by the two color components (called C1 and C2), corresponding to the two largest eigenvalues of the RGB pixel covariance matrix. The second processing phase corresponds to Principal Component Analysis (PCA) for each color channel. Third stage corresponds to the feature fusion of the C1 and C2 PCA-components. Last processing stage is a multiple neural classifier consisting of a set of concurrent self-organizing modules. The proposed system is experimented for the Essex color face database containing 3520 color images of 151 subjects. Key-words: color face recognition, 2D color feature space, feature fusion, concurrent self-organizing maps
منابع مشابه
An Optimum 2D Color Space for Pattern Recognition
This paper presents an optimum color conversion from the 3D RGB space into a 2D selected space to the purpose of pattern recognition. The method is based on the Karhunen-Loève transform (KLT), also known as Principal Component Analysis (PCA). The resulted 2D space is defined by the two color components (called C1 and C2), corresponding to the two largest eigenvalues of the RGB pixel covariance ...
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