The Video Image Based On Lpp Head Pose Estimation Method

Head pose estimation is an important research issue in attention test, behavioral detection, and face recognition. Locality Preserving Projection method provides a fresh idea for us to do nonlinear dimensionality. As a linear dimension reduction tool, Locality Preserving Projection method has great potential to be applied in head pose estimation.This research concentrates on Supervised Locality Preserving Projection and Outlier Measure method to start. Aiming at the problems of the high head pose estimation error and the noise sensitivity of unsupervised LPP, Sinusoidal offset distance method and weighted PCA method was used to estimate the head pose. By this algorithm, the head poses of the training samples are firstly labeled, and all the biased distances between the head poses are calculated. And then the outliers of the head samples are detected, so as to train a better linear mapping matrix, then using the Improved LPP to reduce the dimensions of the image; the SVM classifier are finally used to estimate the head pose.In this paper, the Improved LPP method is used to estimate the head pose. Sinusoidal offset distance method and weighted PCA method not only effectively eliminate the impact of the identity, but also effectively reduce the impact of illumination, facial expression changes and noise. The head pose estimation experiments show that the improved LPP achieves the better results than the traditional LPP and LEA algorithms in both static head pose database and dynamic video stream.