Facial expression recognition based on boostingTree

Abstract

In recent years, facial expression recognition has become an active research area that finds potential applications in the fields such as images processing and pattern recognition, and it plays a very important role in the applications of human-computer interfaces and human emotion analysis. This paper proposes an algorithm called BoostingTree, which is based on the conventional Adaboost and uses tree-structure to convert seven facial expressions to six binary problems, and also presents a novel method to compute projection matrix based on Principal Component Analysis (PCA). In this novel method, a block-merger combination is designed to solve the "data disaster" problem due to the combination of eigenvectors. In the experiment, we construct the weak classifiers set based on this novel method. The weak classifiers selected from the above set by Adaboost are combined into strong classifier to be as node classifier of one level of the tree structure. N-level tree structure built by BoostingTree can effectively solve multiclass problem such as facial expression recognition © Springer-Verlag Berlin Heidelberg 2006.