Comparison of statistical classifiers as applied to the face recognition system based on active shape models

In this paper, a face recognition algorithm based on statistical model of Active Shape (ASM) is presented. A 31 degree-of-freedom shape model was used. The model was derived from a set of 183 faces shapes and named the learning set. Criteria of selection of face to model classifiers were evaluated. Classification was implemented in the shape space, in its Principal Component Analysis (PCA) and Multiple Discriminant Analysis (MDA) transformations. In the shape space the Euclidean and Mahalanobis metrics were used. Euclidean metric was used in PCA and MDA spaces as well. The results were based on experiments carried out on the set of 651 images of eight persons. Further proceedings in the case of ambiguous classification results were suggested.