Study Of Human Face Recognition Methods Based On Subspace Geometrical Feature Analysis

The human face recognition technology has been applied in many areas with developing of social science and economy. It has become a hotspot in pattern recognition study. This thesis mainly focuses on theory and method of human face recognition, especially on feature extraction method based on subspace analysis and geometrical classifier in high dimensional feature space.Feature extraction based on subspace analysis has advantages such as high computing efficency and strong geometry feature description ability. Eigenface method based on principal component analysis and the method based on independent component analysis are two succuessful ones. While because of the disturbance coming from environment, one-order feature extraction based on PCA or ICA can hardly get the features exactly standing for identification. This thesis proposes mixed ICA feature extraction method that integrates second-order analysis with the high order infomation extraction abilitty of ICA. Experiments prove that using different classifier, mixed ICA feature extraction method has more excellent performance than traditional subspace analysis method.On recognition classifier, based on tunable nearest neighbor classifier, the tunable nearest neighbor plane classifier is proposed, which extends the concept of feature subspace building from two dimension to three dimension. Through the tunable parameter, we can optimize the description of the nonlinear distributing of feature points in high dimensional feature space, and get better performance. Through experiment, this method performs more efficient than traditional methods on recognition rate, stability and applicability.In the finality, the thesis is summarized firstly, and then the problems requiring further studies are discussed.