Fully Automatic Face Recognition from 3D Videos

Almost all of the existing research on 3D face recognition is based on static 3D images. 3D videos are believed to provide more information in terms of both the shape as well as the dynamics of an individual's face. This paper presents a system which exploits the spatiotemporal information in 3D videos for the task of face recognition. An algorithm for automatic normalization of raw 3D videos is also given. After the detection of the nose tip, all meshes of the 3D video are cropped and uniformly sampled to form range videos. Spatiotemporal Local Binary Pattern (LBP) descriptors are used for feature extraction from the range videos. For classification, a linear multiclass Support Vector Machine (SVM) is used. The system is trained on videos of a person with different facial expressions and tested on a video with new facial expression. Experimental results on the largest currently available 3D video database, BU_4DFE, show a high recognition rate of 92.68%.