Research On Face Feature Subspace Methods

The research of face recognition and facial expression recognition can be divided into three steps: face detection, face feature extraction and classify and recognition. The extracting of face feature is the key tache of the whole course. Based on the subspace methods of the pattern recognition as the research field, this paper has studied the face feature extraction technology.The main point of subspace method is to find a subspace which can show the features of face images. Different methods have different characters. Among the methods PCA ICA NMF and LNMF, comparatively, PCA extracts the whole information of the images, but the other three methods can extract the local information of the images, which LNMF has the best ability to get local information. In the practicality applications, all the four methods can be used into face recognition and facial expression recognition, and the recognition rates have relationship with the number of base vectors and iterance. LNMF needs the least number of base vectors to reach convergence and reconstruction. When the number of base vectors is the same, LNMF has the best recognition rate. But it needs more number of iterance than the other three methods.The features extracted by subspace can usually show the information of face images, but are not always suitable for classify. The discriminant analysis can be summed-up as: find a function which can return a measurement value, and the value can distinguish different samples. It can train the classifier, as well as extracting features. Thereby, discriminant analysis can be considered as a supervise learning or a method of feature extracting. NKFDA is a Fisher discriminant analysis combined null-space and kernel function, it can select the features which are suitable for classify. So it can be combined with subspace methods.It has been proved that subspace methods combined NKFDA has validity and rationality, based on the face recognition experiments on ORL database and facial expression recognition experiments on Cohn-Kanade database. This method has better results than using subspace methods only. And the method LNMF combined NKFDA has the best result of all the four methods. At the meanwhile, it is robust to illumination and resolution.