Abstract
Three-dimensional (3D) faces are increasingly utilized in many face-related tasks. Despite the promising improvement achieved by 3D face technology, it is still hard to thoroughly evaluate the performance and effect of 3D face technology in real-world applications where variations frequently occur in pose, illumination, expression and many other factors. This is due to the lack of benchmark databases that contain both high precision full-view 3D faces and their 2D face images/videos under different conditions. In this paper, we present such a multi-dimensional face database (namely Multi-Dim) of high precision 3D face scans, high definition photos, 2D still face images with varying pose and expression, low quality 2D surveillance video clips, along with ground truth annotations for them. Based on this Multi-Dim face database, extensive evaluation experiments have been done with state-of-the-art baseline methods for constructing 3D morphable model, reconstructing 3D faces from single images, 3D-assisted pose normalization for face verification, and 3D-rendered multiview gallery for face identification. Our results show that 3D face technology does help in improving unconstrained 2D face recognition when the probe 2D face images are of reasonable quality, whereas it deteriorates rather than improves the face recognition accuracy when the probe 2D face images are of poor quality. We will make Multi-Dim freely available to the community for the purpose of advancing the 3D-based unconstrained 2D face recognition and related techniques towards real-world applications.
Published Version
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