3D facial image analysis for human identification

Presents a new method based on 3D facial section analysis for human face identification. The range data on different curves of intersection, such as, vertical, horizontal, and circular, with the 3D face are used as the distinguishing surface features. These are robust against changes in makeup and lighting as compared to the 2D approach, and more efficient for computation and data storage than other 3D approaches. The performance of this method for human face identification is evaluated through several matching experiments. It has been observed that the curves of intersection crossing the facial central area vertically and contain the features, like, nose and mouth, have the major distinctiveness. Those crossing near the inner corners of the eyes and a part of the nose are also effective for human face identification. But it seems that such an effectiveness requires the accurate extraction of the sections against their locational variations