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.

Increasing 3D Resolution of Kinect Faces / Berretti, S.; Pala, P.; Del Bimbo, A.. - STAMPA. - 8925:(2015), pp. 639-653. (Intervento presentato al convegno ChaLearn Looking at People @ ECCV2014: Challenge and Workshop on Pose Recovery, Action and Gesture Recognition tenutosi a Zurich, Switzerland nel September 6-7, 2014) [10.1007/978-3-319-16178-5_45].

Increasing 3D Resolution of Kinect Faces

BERRETTI, STEFANO;PALA, PIETRO;DEL BIMBO, ALBERTO
2015

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.
2015
Computer Vision - ECCV 2014 Workshops
ChaLearn Looking at People @ ECCV2014: Challenge and Workshop on Pose Recovery, Action and Gesture Recognition
Zurich, Switzerland
September 6-7, 2014
Berretti, S.; Pala, P.; Del Bimbo, A.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/949132
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