  Face recognition is one of the most studied research topics in the community.
In recent years, the research on face recognition has shifted to using 3D
facial surfaces, as more discriminating features can be represented by the 3D
geometric information. This survey focuses on reviewing the 3D face recognition
techniques developed in the past ten years which are generally categorized into
conventional methods and deep learning methods. The categorized techniques are
evaluated using detailed descriptions of the representative works. The
advantages and disadvantages of the techniques are summarized in terms of
accuracy, complexity and robustness to face variation (expression, pose and
occlusions, etc). The main contribution of this survey is that it
comprehensively covers both conventional methods and deep learning methods on
3D face recognition. In addition, a review of available 3D face databases is
provided, along with the discussion of future research challenges and
directions.
