Abstract | ||
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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. |
Year | DOI | Venue |
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2014 | 10.1007/978-3-319-16178-5_45 | COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I |
Keywords | Field | DocType |
Kinect camera, 3D super-resolution, 2D box-splines | Computer vision,Facial recognition system,Computer science,Artificial intelligence | Conference |
Volume | ISSN | Citations |
8925 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 18 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
S. Berretti | 1 | 275 | 24.53 |
Pietro Pala | 2 | 1239 | 91.64 |
Alberto Del Bimbo | 3 | 3777 | 420.44 |