Abstract
paper evaluates the performance of face recognition with different CIE color spaces. The XYZ and L*a*b* color spaces are compared with the gray image (luminance information Y). The face recognition system consists of a feature extraction step and a classification step. The Kernel-PCA is used to construct the feature space. Kernel-PCA is a nonlinear form of Principal Component Analysis (PCA). The k-nearest neighbor classifier with cosine measure is used in the classification step. Experiments using FEI color database with 200 subjects, show that the b* color component can improve the recognition rate.
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© 2014 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Ebied, H.M. (2014). Evaluation of CIE-XYZ System for Face Recognition Using Kernel-PCA. In: Das, V.V., Elkafrawy, P. (eds) Signal Processing and Information Technology. SPIT 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-319-11629-7_20
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DOI: https://doi.org/10.1007/978-3-319-11629-7_20
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