The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and 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)
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
(2D)2LDA: An efficient approach for face recognition
Pattern Recognition
Measure of image sharpness using eigenvalues
Information Sciences: an International Journal
Generalized elastic graph matching for face recognition
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Eigenfeature Regularization and Extraction in Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
Face recognition based on 2D images under illumination and pose variations
Pattern Recognition Letters
Face recognition based on the multi-scale local image structures
Pattern Recognition
Expert Systems with Applications: An International Journal
Iris recognition using shape-guided approach and game theory
Pattern Analysis & Applications
BDPCA plus LDA: a novel fast feature extraction technique for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Pose-robust face recognition via sparse representation
Pattern Recognition
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In this paper, a simple technique is proposed for face recognition among many human faces. It is based on the polynomial coefficients, covariance matrix and algorithm on common eigenvalues. The main advantage of the proposed approach is that the identification of similarity between human faces is carried out without computing actual eigenvalues and eigenvectors. A symmetric matrix is calculated using the polynomial coefficients-based companion matrices of two compared images. The nullity of a calculated symmetric matrix is used as similarity measure for face recognition. The value of nullity is very small for dissimilar images and distinctly large for similar face images. The feasibility of the propose approach is demonstrated on three face databases, i.e., the ORL database, the Yale database B and the FERET database. Experimental results have shown the effectiveness of the proposed approach for feature extraction and classification of the face images having large variation in pose and illumination.