3D Aided Face Recognition across Pose Variations

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.
Type de document :
Chapitre d'ouvrage
Biometric Recognition, Springer, pp.58-66, 2012, 〈10.1007/978-3-642-35136-5_8〉
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Soumis le : mercredi 10 août 2016 - 16:25:55
Dernière modification le : mercredi 31 octobre 2018 - 12:24:25



Wuming Zhang, Di Huang, Yunhong Wang, Liming Chen. 3D Aided Face Recognition across Pose Variations. Biometric Recognition, Springer, pp.58-66, 2012, 〈10.1007/978-3-642-35136-5_8〉. 〈hal-01353184〉



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