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Face recognition is of paramount importance in computer vision and biometrics systems. In this paper we propose an improved method which is suitable to handle variations in image configurations like pose, illumination, and facial expressions as well as occlusion and disguise, in order to provide high efficien-cyi in the face recognition. This method integrates the low-rank matrix which is recovered...
In this paper we propose a novel feature selection method based on linear discriminant analysis (LDA). To view feature selection as a numerical computation problem, the paper shows, for the first time, that it is feasible to employ LDA for feature selection. The proposed method also shows that different components statistically have different effects on the feature selection result, which can be evaluated...
Xu et al (A novel method for Fisher discriminant Analysis. Pattern Recognition, 2004, 37 (2): 381-384) proposed an interesting Fisher discriminant analysis method that not only requires that discriminant vectors have large Fisher criterion values but also requires that resultant features have small statistical correlation to each other. Indeed, this method is motivated by the following cognition:...
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