Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Discriminant Eigenfeatures for Image Retrieval
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
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Face Recognition: Features Versus Templates
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
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Journal of Cognitive Neuroscience
Face recognition technique using symbolic PCA method
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Robust coding schemes for indexing and retrieval from large face databases
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Symbolic kernel fisher discriminant method with a new RBF kernel function for face recognition
Machine Graphics & Vision International Journal
Augmented Small-Scale Database to Improve the Performance of Eigenface Recognition Technique
International Journal of Computer Vision and Image Processing
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Techniques that can introduce low dimensional feature representation with enhanced discriminatory power are important in face recognition systems. This paper presents one of the symbolic factor analysis method i.e., symbolic Linear Discriminant Analysis (symbolic LDA) method for face representation and recognition. Classical factor analysis methods extract features, which are single valued in nature to represent face images. These single valued variables may not be able to capture variation of each feature in all the images of same subject; this leads to loss of information. The symbolic Linear Discriminant Analysis Algorithm extracts most discriminating interval type features; they optimally discriminate among the classes represented in the training set. The proposed method has been successfully tested for face recognition using two databases, ORL and Yale Face database. The effectiveness of the proposed method is shown in terms of comparative performance against popular classical factor analysis methods such as eigenface method and Linear Discriminant Analysis method. Experimental results show that symbolic LDA outperforms the classical factor analysis methods.