Feature extraction using kernel inverse FDA

This paper presents a new feature extraction method called kernel inverse Fisher discriminant analysis for face recognition. In the method, the nonlinear kernel trick is first applied to map the input data into an implicit feature space. Then the inverse Fisher discriminant analysis is used to analyze the data for producing nonlinear discriminating features Experimental results on ORL face database show that the proposed method is effective in classifying.