Color Face Recognition by Hypercomplex Gabor Analysis
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Optimization of color conversion for face recognition
EURASIP Journal on Applied Signal Processing
Bidirectional PCA with assembled matrix distance metric for image recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Color Image Discriminant Models and Algorithms for Face Recognition
IEEE Transactions on Neural Networks
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There are several methods to recognize and reconstruct a human face image. The principal component analysis (PCA) is a successful approach because of its effective extraction of the global feature and excellent reconstruction of face image. However, the crucial shortcomings of PCA are its low recognition rate and overfitting of feature extraction which leads to the dependence of training data on training samples. In this paper, a modified two-dimension principal component analysis (2DPCA) and bidirectional principal component analysis (BDPCA) methods based on quaternion matrix are proposed to recognize and reconstruct a color face image. In these methods, the spatial distribution information of color images is used to represent a color face, and the 2DPCA or BDPCA feature of color face image is extracted by reducing the dimensionality in both column and row directions. A method obtaining orthogonal eigenvector set of quaternion matrix is proposed. Numerous experiments show that the present approach based on quaternion matrix can effectively smooth the overfitting issue and substantially enhance the recognition rate.