Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
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
Robust coding schemes for indexing and retrieval from large face databases
IEEE Transactions on Image Processing
Two-dimensional maximum margin feature extraction for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
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In this paper, a novel image discriminant analysis method, coined two-dimensional discriminant transform based on scatter difference criterion (2DSDD), is developed for image representation. The proposed 2DSDD scheme adopts the difference of both between-class scatter and within-class scatter as discriminant criterion. In this way, the small sample size problem usually occurred in the traditional Fisher discriminant analysis (LDA) is essentially avoided. In addition, the developed method directly depends on image matrices. That is to say, it is not necessary to convert the image matrix into high-dimensional image vector like those conventional linear discriminant methods prior to feature extraction so that much computational time would be saved. Finally, the experimental results on the ORL face database indicate that the proposed method outperforms Fisherfaces, the standard scatter difference discriminant analysis, not only in the computation efficiency, but also in its recognition performance.