3D Face Recognition: A Survey
It provides a comprehensive overview for researchers in computer vision and biometrics, but it is incremental as it synthesizes existing work without introducing new methods.
This survey reviews 3D face recognition techniques from the past decade, categorizing them into conventional and deep learning methods and evaluating their accuracy, complexity, and robustness to variations like expression and pose.
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.