Head Pose Estimation Based on Random Forests for Multiclass Classification

Abstract

Head pose estimation remains a unique challenge for computer vision system due to identity variation, illumination changes, noise, etc. Previous statistical approaches like PCA, linear discriminative analysis (LDA) and machine learning methods, including SVM and Adaboost, cannot achieve both accuracy and robustness that well. In this paper, we propose to use Gabor feature based random forests as the classification technique since they naturally handle such multi-class classification problem and are accurate and fast. The two sources of randomness, random inputs and random features, make random forests robust and able to deal with large feature spaces. Besides, we implement LDA as the node test to improve the discriminative power of individual trees in the forest, with each node generating both constant and variant number of children nodes. Experiments are carried out on two public databases to show the proposed algorithm outperforms other approaches in both accuracy and computational efficiency.