A solution of combining several classifiers for face recognition

Face recognition has been of interest to a growing number of researchers due to its applications on security. Within past years, numerous face recognition algorithms have been proposed by researchers. However, there is no evidence that shows one specific proposed method is the best under all circumstances. So, a combination of several methods can be a good approach. Committee machine structures, which were introduced in the machine learning community, show some ways to combine different methods in a single framework. A committee machine structure makes decision according to its components. In the previous face recognition methods with committee machines, only the information which is extracted from test phase is used for Combining classifiers. In this paper, in addition to the information in test phase, training phase information is used for combining classifiers. For this purpose, we introduce a new unit which is called "Region Finder". This unit is attached to each classifier in a committee machine structure and is learned based on train phase information. A region finder determines its classifier recognition power in the classifier feature space. We applied our idea to a structure of five well-known classifiers, PCA, ICA, LDA, SVM and neural networks which are implemented for face recognition. Comparative experimental results of our committee machine with different algorithms and the structure without region finder units, demonstrate that the proposed system achieves improved accuracy.