3D Face Recognition: Two Decades of Progress and Prospects

Abstract

3D face recognition has been extensively investigated in the last two decades due to its wide range of applications in many areas such as security and forensics. Numerous methods have been proposed to deal with the challenges faced by 3D face recognition such as facial expressions, pose variations and occlusions. These methods have achieved superior performances on several small-scale datasets including FRGC v2.0, Bosphorus, BU-3DFE, and Gavab. However, deep learning based 3D face recognition methods are still in their infancy due to the lack of large-scale 3D face datasets. To stimulate future research in this area, we present a comprehensive review of the progress achieved by both traditional and deep learning based 3D face recognition methods in the last two decades. Moreover, comparative results on several publicly available datasets under different challenges of facial expressions, pose variations and occlusions are also presented.

Multimodal Face Data Sets—A Survey of Technologies, Applications, and Contents

“…By capturing the three-dimensional structure of the face, including the contours, shape, and spatial relationships of facial features, 3D systems can create a more accurate and unique facial signature for each individual. When comparing 2D and 3D facial analysis accuracy under identical pose and lighting conditions, 3D face recognition often outperforms its 2D counterpart [159]. Table 5 summarizes 3D data sets The point cloud representation is the most fundamental way to depict the facial surface.…”