Increasing 3D resolution of kinect faces

Abstract

Performing face recognition across 3D scans of different resolution is now attracting an increasing interest thanks to the introduction of a new generation of depth cameras, capable of acquiring color/depth images over time. However, these devices have still a much lower resolution than the 3D high-resolution scanners typically used for face recognition applications. Due to this, comparing low- and high-resolution scans can be misleading. Based on these considerations, in this paper we define an approach for reconstructing a higher-resolution 3D face model from a sequence of low-resolution 3D scans. The proposed solution uses the scaled ICP algorithm to align the low-resolution scans with each other, and estimates the value of the high-resolution 3D model through a 2D Box-spline approximation. The approach is evaluated on the The Florence face dataset that collects high- and low-resolution data for about 50 subjects. Measures of the quality of the reconstructed models with respect to high-resolution scans and in comparison with two alternative techniques, demonstrate the viability of the proposed solution.