HVPN: The Combination of Horizontal and Vertical Pose Normalization for Face Recognition

Face recognition has received much attention with numerous applications in various fields. Although many face recognition algorithms have been proposed, usually they are not highly accurate enough when the poses of faces vary considerably. In order to solve this problem, some researches have proposed pose normalization algorithm to eliminate the negative effect cause by poses. However, only horizontal normalization has been considered in these researches. In this paper, the HVPN (Horizontal and Vertical Pose Normalization) system is proposed to accommodate the pose problem effectively. A pose invariant reference model is re-rendered after the horizontal and vertical pose normalization sequentially. The proposed face recognition system is evaluated based on the face database constructed by our self. The experimental results demonstrate that pose normalization can improve the recognition performance using conventional principal component analysis (PCA) and linear discriminant analysis (LDA) approaches under varying pose. Moreover, we show that the combination of horizontal and vertical pose normalization can be evaluated with higher performance than mere the horizontal pose normalization.