Abstract:
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.
