Probabilistic Reasoning Models for Face Recognition

We introduce in this paper two probabilistic reasoning models (PRM-1 and PRM-2) which combine the Principal Component Analysis (PCA) technique and the Bayes classifier and show their feasibility on the face recognition problem. The conditional probability density function for each class is modeled using the within class scatter and the Maximum A Posteriori (MAP) classification rule is implemented in the reduced PCA subspace. Experiments carried out using 1107 facial images corresponding to 369 subjects (with 169 subjects having duplicate images) from the FERET database show that the PRM approach compares favorably against the two well-known methods for face recognition --- the Eigenfaces and Fisherfaces. 1. Introduction A successful face recognition methodology depends heavily on the particular choice of the features used by the (pattern) classifier [3], [14], [2]. Feature selection in pattern recognition involves the derivation of salient features from the raw input data in order to ..