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A new face recognition algorithm, termed enhanced marginal fisher analysis (EMFA), is proposed in the paper. Different from MFA in which the construction of the interclass graph is based on the whole dataset, that is usually time-consuming, EMFA first find the nearest classes of each class using the mean vector of each class, then the marginal points can be(More)
The technique of two-dimensional principal component analysis (2DPCA) is analyzed and its essence is revealed. The image total scatter matrix of 2DPCA is in nature equivalent to the sum of all total scatter matrices of m training subsets in which the kth subset is formed by the kth line of each of all training images, where m is the number of lines(More)
For face recognition, feature extraction and learning robust and discriminative structure are the most important. Since Qinfeng Shi et al have demonstrated that ¦É2 approach to the face recognition problem is not only more accurate but also more robust and much faster. Motivated above, we propose a novel feature extraction method by combining(More)
—Emerging new behavioral monitoring and tracking technologies that use camera networks offer unprecedented capabilities for health-care monitoring. A challenging problem is to track people through very sparse sensor measurements to reduce the cost of expensive sensors and be robust to sensor failure. In this paper we propose a Coupled State Space Model(More)
Two-dimensional scatter-difference discriminant analysis not only essentially avoids the small sample size problem which occurred in traditional Fisher discriminant analysis, but also saves much computational time for feature extraction. In this paper, by analyzing the equivalence and the intrinsic essence between two-dimensional scatter-difference(More)