Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using the Discrete Cosine Transform
International Journal of Computer Vision - Special issue: Research at McGill University
Features for robust face-based identity verification
Signal Processing
Discriminant Analysis of Principal Components for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Comparison of Eigenface-Based Feature Vectors under Different Impairments
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Pattern Recognition Letters
Journal of Cognitive Neuroscience
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
A face and palmprint recognition approach based on discriminant DCT feature extraction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On image matrix based feature extraction algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
BDPCA plus LDA: a novel fast feature extraction technique for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
Improving kernel Fisher discriminant analysis for face recognition
IEEE Transactions on Circuits and Systems for Video Technology
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Face recognition by applying wavelet subband representation and kernel associative memory
IEEE Transactions on Neural Networks
High-speed face recognition based on discrete cosine transform and RBF neural networks
IEEE Transactions on Neural Networks
Bilinear Analysis for Kernel Selection and Nonlinear Feature Extraction
IEEE Transactions on Neural Networks
A sub-block-based eigenphases algorithm with optimum sub-block size
Knowledge-Based Systems
Discriminative Zernike and Pseudo Zernike Moments for Face Recognition
International Journal of Computer Vision and Image Processing
Video genre classification using weighted kernel logistic regression
Advances in Multimedia - Special issue on Multimedia Applications for Smart Device in Ubiquitous Environments
Hi-index | 0.01 |
Discrete cosine transform (DCT) is a powerful transform to extract proper features for face recognition. After applying DCT to the entire face images, some of the coefficients are selected to construct feature vectors. Most of the conventional approaches select coefficients in a zigzag manner or by zonal masking. In some cases, the low-frequency coefficients are discarded in order to compensate illumination variations. Since the discrimination power of all the coefficients is not the same and some of them are discriminant than others, so we can achieve a higher true recognition rate by using discriminant coefficients (DCs) as feature vectors. Discrimination power analysis (DPA) is a statistical analysis based on the DCT coefficients properties and discrimination concept. It searches for the coefficients which have more power to discriminate different classes better than others. The proposed approach, against the conventional approaches, is data-dependent and is able to find DCs on each database. The simulations results of the various coefficient selection (CS) approaches on ORL and Yale databases confirm the success of the proposed approach. The DPA-based approaches achieve the performance of PCA/LDA or better with less complexity. The proposed method can be implemented for any feature selection problem as well as DCT coefficients. Also, a new modification of PCA and LDA is proposed namely, DPA-PCA and DPA-LDA. In these modifications DCs which are selected by DPA are used as the input of these transforms. Simulation results of DPA-PCA and DPA-LDA on the ORL and Yale database verify the improvement of the results by using these new modifications.