Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Recognizing faces with PCA and ICA
Computer Vision and Image Understanding - Special issue on Face recognition
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Representation By Using Non-tensor Product Wavelets
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Texture Image Retrieval Using Novel Non-separable Filter Banks Based on Centrally Symmetric Matrices
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Rapid and brief communication: Face recognition using common faces method
Pattern Recognition
Robust recursive least squares learning algorithm for principal component analysis
IEEE Transactions on Neural Networks
Algorithms for accelerated convergence of adaptive PCA
IEEE Transactions on Neural Networks
An Algorithm for License Plate Recognition Applied to Intelligent Transportation System
IEEE Transactions on Intelligent Transportation Systems
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In this paper, a novel approach for face recognition based on the difference vector plus kernel PCA is proposed. Difference vector is the difference between the original image and the common vector which is obtained by the images processed by the Gram-Schmidt orthogonalization and represents the common invariant properties of the class. The optimal feature vectors are obtained by KPCA procedure for the difference vectors. Recognition result is derived from finding the minimum distance between the test difference feature vectors and the training difference feature vectors. To test and evaluate the proposed approach performance, a series of experiments are performed on four face databases: ORL, Yale, FERET and AR face databases and the experimental results show that the proposed method is encouraging.