Face Infor mation Processing by Fast Statistical Lear ning Algorithm

In this paper, we propose a new statistical learning algorithm. This study quantitatively verifies the effectiveness of its feature extraction perfor mance for face infor mation pr ocessing. Simple-FLDA is an algorithm based on a geometrical analysis of the Fisher linear discriminant analysis. As a high- speed featur e extraction method, the pr esent algor ithm in this paper is the improved version of Simple-FLDA. First of all, the appr oximated principal component analysis (learning by Simple- PCA) that uses the mean vector of each class is calculated. Next, in or der to adj ust within-class variance in each class to 0, the vectors in the class are removed. By this processing, it becomes high-speed feature extraction method than Simple-FLDA. The effectiveness is verified by computer simulations using face images.