A SMALL SAMPLE FACE RECOGNITION STATISTICAL LEARNING METHOD BASED ON VIRTUAL SAMPLES

The face recognition approach is studied. A small sample face recognition approach based on Bayes is presented. In this method, prototype faces are used to create virtual samples in order to expand the sample number. Then, the PCA method is used to reduce dimensions and extract features. BEM algorithm learns the parameters of condition probability function of class features. A Bayes classifier is constructed to recognize face. The method combines the principal component analysis technique and the Bayes classifier and shows their feasibility on the face recognition problem. It also overcomes the problem of limited samples in pattern recognition application and improves the recognition rate of small samples face recognition. The method can also be used in other objects recognition. Experimental result shows the approach can improve face recognize rate of small sample efficiently.