A new method called kernel-based Fisher minimum discriminant analysis was developed to improve face recognition by extracting more effective classification information. This method overcomes the limitations of previous techniques by allowing for the extraction of nonlinear features without being restricted by the number of classes. Experiments on face databases demonstrated the effectiveness of this approach.