Face Recognition by Super-Resolved 3D Models From Consumer Depth Cameras

Face recognition based on the analysis of 3D scans has been an active research subject over the last few years. However, the impact of the resolution of 3D scans on the recognition process has not been addressed explicitly, yet being an element of primal importance to enable the use of the new generation of consumer depth cameras for biometric purposes. In fact, these devices perform depth/color acquisition over time at standard frame-rate, but with a low resolution compared to the 3D scanners typically used for acquiring 3D faces in recognition applications. Motivated by these considerations, in this paper, we define a super-resolution approach for 3D faces by which a sequence of low-resolution 3D face scans is processed to extract a higher resolution 3D face model. The proposed solution relies on the scaled iterative closest point procedure to align the low-resolution scans with each other, and estimates the value of the high-resolution 3D model through a 2D box-spline functions approximation. To evaluate the approach, we built-and made it publicly available-the Florence Superface dataset that collects high-resolution and low-resolution data for about 50 different persons. Qualitative and quantitative results are reported to demonstrate the accuracy of the proposed solution, also in comparison with alternative techniques.