Abstract. In this paper we approach the problem of head pose estimation by combining Multi-scale Gaussian Derivatives with Support Vector Machines. We evaluate the approach on the Pointing04 and CMU-PIE data sets and to estimate the pan and tilt of the head from facial images. We achieved a mean absolute error of 6.9 degrees for pan and 8.0 degrees for tilt on the Pointing04 data set.
A combination of Multi-scale Gaussian Derivatives (MGD) and Support Vector Machine is used by Jain et al. REF .
Head Pose Estimation Using Multi-scale Gaussian Derivatives
SCIA
Computer Science