Paper Info
Title
Face Recognition by Super-Resolved 3D Models From Consumer Depth Cameras
Abstract
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.
Year
DOI
Venue
2014
10.1109/TIFS.2014.2337258
IEEE Transactions on Information Forensics and Security
Keywords
Field
DocType
Three-dimensional displays,Face,Image resolution,Solid modeling,Cameras,Face recognition,Image reconstruction
Facial recognition system,Computer vision,Pattern recognition,Computer science,Artificial intelligence,Biometrics,Iterative closest point
Journal
Volume
Issue
ISSN
9
9
1556-6013
Citations 
PageRank 
References 
10
0.45
32
Authors
3
Name
Order
Citations
PageRank
Stefano Berretti188052.33
Pietro Pala2123991.64
Alberto Del Bimbo33777420.44