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
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
Solving the Small Sample Size Problem of LDA
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Optimal Set of Discriminant Vectors
IEEE Transactions on Computers
Journal of Cognitive Neuroscience
Robust coding schemes for indexing and retrieval from large face databases
IEEE Transactions on Image Processing
A shape- and texture-based enhanced Fisher classifier for face recognition
IEEE Transactions on Image Processing
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Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Basically, in LDA the image always needs to be transformed into 1D vector, however recently two-dimensional PCA (2DPCA) technique have been proposed. In 2DPCA, PCA technique is applied directly on the original images without transforming into 1D vector. In this paper, we propose a new LDA-based method that applies the idea of two-dimensional PCA. In addition to that, our approach proposes an method called Discriminative Common Images based on a variation of Fisher’s LDA for face recognition. Experiment results show our method achieves better performance in comparison with the other traditional LDA methods.