Precise estimation of high-dimensional distribution and its application to face recognition

In statistical pattern recognition, it is important to estimate true distribution of patterns precisely to obtain high recognition accuracy. Normal mixtures are sometimes used for representing distributions. However, precise estimation of the parameters of normal mixtures requires a great number of sample patterns, especially for high dimensional vectors. For some pattern recognition problems, such as face recognition, very high dimensional feature vectors are necessary and there are always not enough training samples compared with the dimensionality. We present a method to estimate the distributions based on normal mixtures with small number of samples. The proposed algorithm is applied to face recognition problem which requires high dimensional feature vectors. Experimental results show the effectiveness of the proposed algorithm.