Class-Specific Reference Discriminant Analysis With Application in Human Behavior Analysis
IEEE Transactions on Human-Machine Systems
In this paper, a novel nonlinear subspace learning technique for class-specific data representation is proposed. A novel data representation is obtained by applying nonlinear classspecific data projection to a discriminant feature space, where the data belonging to the class under consideration are enforced to be close to their class representation, while the data belonging to the remaining classes are enforced to be as far as possible from it. A class is represented by an optimized class
... timized class vector, enhancing class discrimination in the resulting feature space. An iterative optimization scheme is proposed to this end, where both the optimal nonlinear data projection and the optimal class representation are determined in each optimization step. The proposed approach is tested on three problems relating to human behaviour analysis: face recognition, facial expression recognition and human action recognition. Experimental results denote the effectiveness of the proposed approach, since the proposed Classspecific Reference Discriminant Analysis outperforms Kernel Discriminant Analysis, Kernel Spectral Regression and Classspecific Kernel Discriminant Analysis, as well as Support Vector Machine-based classification, in most cases.