Face detection based on Kernel Fisher Discriminant analysis

This work presents a face detection method based on kernel Fisher discriminant analysis (KFD). Kernel based methods have been extensively investigated both in theories and applications, such as SVM and kernel PCA. Using the kernel trick, linear Fisher discriminant can be extended to non-linear case. Since the distribution of face patterns is very complex and highly nonlinear, using non-linear classification tools can hopefully tackle the problem of face detection. We explore the application of KFD in the task of frontal face detection. The experimental results prove the effectiveness of KFD in the face detection problem.