Pose-invariant detection of facial features from range data

This paper, firstly, presents a new signature representation for point in range data, called curgram. Curgram serves to describe the structural neighborhood of a point and establish a signature for each of the given 3D data points rather than information of this point position. It yields invariance under rigid motions and mirror imaging. Secondly, its application to detection of facial features from range data is performed by incorporating the metric for curgram into the conventional appearance-based face detection framework. Experimental results with fifteen face range images have demonstrated the validity and effectiveness of the proposed method.