Real Adaboost feature selection for Face Recognition

Abstract

Determining what features are important for face representation is quite challenging in Face Recognition. Real Adaboost performs remarkably in training classifiers for object detection which is a binary classification problem. As for Face Recognition, we should transform the multi-class problem into a binary one. In this paper, a feature selection method based on Real Adaboost for Face Recognition is proposed based on intra-person and extra-person which performs the multi-class-to-binary transformation. It is the major contribution of this paper. Experimental results on the Face Recognition Grand Challenge version 2.0 with comparison to Joint Boosting and Discrete Adaboost confirm the effectiveness of Real Adaboost for Face Recognition.