Markov network-based multiple classifier for face image retrieval

We propose a new face-recognition framework to learn the relationship between multiple classifiers using a Markov network. For each image, we make three face models based on different distances between two eye locations. The novelty of the proposed method lies in that the method not only compares the query and target images at the three different levels, but also takes into account the statistical dependency between the three different models. This dependency is captured by a Markov network, which we describe by a graphical model, where query models are observation nodes, target models are hidden nodes, and the network line represents their relationships. For each observation-hidden node pair, we collect a set of target candidates that are most similar to the observation, and the relationship between the hidden nodes is captured in terms of the similarity between target images. Posterior probabilities at the three hidden nodes of the Markov network are computed by a belief-propagation algorithm. We evaluate the proposed method using FRGC ver 2.0, XM2VTS, BANCA, and PIE databases, which demonstrates its superiority under the untrained variations.