Face recognition has been a hot research area for its wide range of applications [1]. In human identification scenarios, facial metrics are more naturally accessible than many other biometrics, such as iris, fingerprint, and palm print [2]. Face recognition is also highly valuable in human computer interaction, access control, video surveillance, and many other applications. Although 2D face recognition research made significant progresses in recent years, its accuracy is still highly depended on light conditions and human poses [3, 4]. When the light is dim or the face poses are not properly aligned in the camera view, the recognition accuracy will suffer. The fast evolution of 3D sensors reveals a new path for face recognition that could overcome the fundamental limitations of 2D technologies. The geometric information contained in 3D facial data could substantially improve the recognition accuracy under conditions that are difficult for 2D technologies [5]. Many researchers have turned their focuses to 3D face recognition and made this research area a new trend. A general work flow for 3D face recognition is shown in Fig. 1. The work flow could be decomposed into two phases and five stages. In the training phase, 3D face data are acquired and then preprocessed to obtain "clean" 3D faces. Then the data are processed by feature extraction algorithms to find the features that could be used to differentiate faces. The features of each face are then stored into the feature database. In the testing phase, the target face goes through the acquisition, preprocessing, and feature extraction Abstract 3D face recognition has become a trending research direction in both industry and academia. It inherits advantages from traditional 2D face recognition, such as the natural recognition process and a wide range of applications. Moreover, 3D face recognition systems could accurately recognize human faces even under dim lights and with variant facial positions and expressions, in such conditions 2D face recognition systems would have immense difficulty to operate. This paper summarizes the history and the most recent progresses in 3D face recognition research domain. The frontier research results are introduced in three categories: pose-invariant recognition, expression-invariant recognition, and occlusion-invariant recognition. To promote future research, this paper collects information about publicly available 3D face databases. This paper also lists important open problems.

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