Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
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This paper presents a Complete Orthogonal Image discriminant (COID) method and its application to biometric face recognition. The novelty of the COID method comes from 1) the derivation of two kinds of image discriminant features, image regular and image irregular, in the feature extraction stage and 2) the development of the Complete OID (COID) featuresbased on the fusion of the two kinds of image discriminant features used in classification. Firstly, the COID method first derives a feature image of the face image with reduced dimensionality of the image matrix by means of two dimensional principal component analysis and then performs discriminant analysis in a double discriminant subspaces in order to derive the image regular and irregular features making it more suitable for small sample size problem. Finally combines the image regular and irregular features which are complementary for achieving better discriminant features. The feasibility of the COID method has been successfully tested using the ORL images where it was 73.8% more superior to 2DFLD method on face recognition.