Elliptic Metric K-NN Method with Asymptotic MDL Measure

Abstract

We describe an adaptive metric learning model combining the generative and the discriminative models for the face recognition. The asymptotic model based on the MDL measure is formulated for each class to estimate the variance by using small training examples. The feature fusion method is introduced to assume the missing patterns between the classes and to deal with the k-th nearest neighbor classification. The metric parameters obtained from the asymptotic MDL estimation are refined by using the synthesized feature patterns. We demonstrate an improved recognition performance on the ORL and UMIST face databases.