Face pose estimation based on integral slice features of single depth images

Automatic estimation of face pose from images has wide applications in human computer interaction, biometrics, etc. This paper proposes a novel method for accurate face pose estimation from single depth images. Integral slice features are developed to characterize the distinctive and robust patterns of different face poses. Firstly the sampling points on the 3D facial surfaces are divided into multiple slices along the depth axis. And then a segment of facial slices is averaged to get an integral center. The vector from the integral center to a reference point is defined as a pose sensitive feature. Two kinds of reference points are tried in our method, i.e. nose tip and the geometric center of any slice. Therefore a large number of feature vectors can be obtained to describe the distinguishing pattern of each face pose by changing the starting and ending points of the integral segment of facial slices. Finally all these integral slice features are sent to a neural network to train a face pose estimator. Experimental results on the ETH database demonstrate that the proposed method is more accurate than state-of-the-art methods for face pose estimation.