3D aided face recognition across pose variations

Authors:
Wuming Zhang;Di Huang;Yunhong Wang;Liming Chen
Affiliations:
MI Department, LIRIS, CNRS 5205, Ecole Centrale de Lyon, Lyon, France, IRIP, School of Computer Science and Engineering, Beihang Univ., Beijing, China;IRIP, School of Computer Science and Engineering, Beihang Univ., Beijing, China;IRIP, School of Computer Science and Engineering, Beihang Univ., Beijing, China;MI Department, LIRIS, CNRS 5205, Ecole Centrale de Lyon, Lyon, France
Venue:
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Year:
2012

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Abstract

Recently, 3D aided face recognition, concentrating on improving performance of 2D techniques via 3D data, has received increasing attention due to its wide application potential in real condition. In this paper, we present a novel 3D aided face recognition method that can deal with the probe images in different viewpoints. It first estimates the face pose based on the Random Regression Forest, and then rotates the 3D face models in the gallery set to that of the probe pose to generate specific gallery sample for matching, which largely reduces the influence of head pose variations. Experiments are carried out on a subset of the FRGC v1.0 database, and the achieved performance clearly highlights the effectiveness of the proposed method.