Kernel-based subspace analysis for face recognition

In face recognition, if the extracted input data contains misleading information (uncertainty), the classifiers may produce degraded classification performance. In this paper, we employed kernel-based discriminant analysis method for the non-separable problems in face recognition under facial expression changes. The effect of the transformations on a subsequent classification was tested in combination with learning algorithms. We found that the transformation of kernel-based discriminant analysis has a beneficial effect on the classification performance. The experimental results indicated that the nonlinear discriminant analysis method dealt with the uncertainty problem very well. Facial expressions can be used as another behavior biometric for human identification. It appears that face recognition may be robust to facial expression changes, and thus applicable.