Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Rapid and brief communication: Two-dimensional FLD for face recognition
Pattern Recognition
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
Matrix-pattern-oriented least squares support vector classifier with AdaBoost
Pattern Recognition Letters
Person Specific Document Retrieval Using Face Biometrics
ICADL 08 Proceedings of the 11th International Conference on Asian Digital Libraries: Universal and Ubiquitous Access to Information
2 directional 2 dimensional pairwise FLD for handwritten Kannada numeral recognition
ICADL'07 Proceedings of the 10th international conference on Asian digital libraries: looking back 10 years and forging new frontiers
Matrix pattern based minimum within-class scatter support vector machines
Applied Soft Computing
Multi-linear neighborhood preserving projection for face recognition
Pattern Recognition
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In this paper, a new technique called 2-directional 2-dimensional Fisher's Linear Discriminant analysis ((2D)^2 FLD) is proposed for object/face image representation and recognition. We first argue that the standard 2D-FLD method works in the row direction of images and subsequently we propose an alternate 2D-FLD which works in the column direction of images. To straighten out the problem of massive memory requirements of the 2D-FLD method and as well the alternate 2D-FLD method, we introduce (2D)^2 FLD method. The introduced (2D)^2 FLD method has the advantage of higher recognition rate, lesser memory requirements and better computing performance than the standard PCA/2D-PCA/2D-FLD method, and the same has been revealed through extensive experimentations conducted on COIL-20 dataset and AT&T face dataset.