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
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Video Indexing and Full-Video Search for Object Appearances
Proceedings of the IFIP TC2/WG 2.6 Second Working Conference on Visual Database Systems II
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Centroid neural network with chi square distance measure for texture classification
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Eigenspace-based face recognition: a comparative study of different approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Face Recognition by Regularized Discriminant Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Centroid neural network for unsupervised competitive learning
IEEE Transactions on Neural Networks
Weighted centroid neural network for edge preserving image compression
IEEE Transactions on Neural Networks
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An unsupervised competitive neural network for efficient recognition of facial images is proposed. The proposed unsupervised competitive neural network, called centroid neural network with Chi square distance measure (CNN-χ 2), employs the Chi square measure as its distance measure and utilizes the local binary pattern (LBP) as an effective feature extraction tool for image data. The proposed CNN-χ 2is applied to a face recognition problem on the Yale face database. The results are compared with those of well-known approaches including KFD (Kernel Fisher Discriminant based on eigenfaces), RDA (Regularized Discriminant Analysis), and Sobel faces combined with 2DPCA (two dimensional Principle Component Analysis). The evaluated results demonstrate that the proposed CNN-χ2 algorithm outperforms recent state-of-art algorithms in terms of recognition rate.