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
The FERET Evaluation Methodology for Face-Recognition Algorithms
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D and 3D face recognition: A survey
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Median MSD-based method for face recognition
Neurocomputing
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Computer Vision and Image Understanding
Face recognition using a fuzzy fisherface classifier
Pattern Recognition
Feature extraction based on fuzzy 2DLDA
Neurocomputing
Weighted maximum scatter difference based feature extraction and its application to face recognition
Machine Vision and Applications
Robust coding schemes for indexing and retrieval from large face databases
IEEE Transactions on Image Processing
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To improve the recognition performance of maximum scatter difference (MSD), a fuzzy MSD method is proposed in this paper. In the existing MSD model, the class mean vector in the expressions of within-class scatter matrix and between-class scatter matrix is estimated by class sample average. Obviously, the class sample average is not sufficient to provide an accurate estimate of the class mean using a few of the given samples, because there will be some outliers in the sample set under the non-ideal conditions such as variations of expression, illumination, pose, and so on. As a result, the recognition performance of traditional MSD model will decrease. To address this problem, inspired by existing fuzzy application, the fuzzy theory is incorporated into traditional maximum scatter difference algorithm. In this method, applying fuzzy K-nearest neighbor (FKNN), the membership degree matrix of training samples is calculated, which is used to get fuzzy means of each class and the average of fuzzy means is then applied to the definition of within-class scatter matrix and between class scatter difference matrix, respectively. The results of experiments conducted on ORL, YALE and FERET face database indicate the effectiveness of the proposed approach.