Head Pose Recognition Based on 2-D KPCA

摘要

One's head pose can be estimated using face images. The hidden manifold of head pose in the high dimensional space can be successfully embedded into a 2 dimensional space using Kernel Principal Component Analysis (KPCA). A pose curve is gotten using KPCA train samples and new pose image is projected onto this curve. The pose angle can be estimated using interpolation method. The disadvantage of traditional linear method is conquered by using 2-D KPCA and the experimental results that the method is effective to estimate head poses. The kernel functions' effects on estimation accuracy are also discussed.