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ROBUST REPRESENTATION OF 3D FACES FOR RECOGNITION

Artificial Vision and Biometry Group — GAVAB, Escuela Superior de Ciencias Experimentales y Tecnología, Universidad Rey Juan Carlos, C/ Tulipán s/n 28933, Móstoles, Madrid, Spain

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ÁNGEL SÁNCHEZ

Artificial Vision and Biometry Group — GAVAB, Escuela Superior de Ciencias Experimentales y Tecnología, Universidad Rey Juan Carlos, C/ Tulipán s/n 28933, Móstoles, Madrid, Spain

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ENRIQUE FRÍAS-MARTÍNEZ

Artificial Vision and Biometry Group — GAVAB, Escuela Superior de Ciencias Experimentales y Tecnología, Universidad Rey Juan Carlos, C/ Tulipán s/n 28933, Móstoles, Madrid, Spain

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https://doi.org/10.1142/S0218001406005198Cited by:10 (Source: Crossref)

Abstract

Automatic face recognition is becoming increasingly important due to the security applications derived from it. Although the facial recognition problem has focused on 2D images, recently, due to the proliferation of 3D scanning hardware, 3D face recognition has become a feasible application. This 3D approach does not need any color information. In this way, it has the following main advantages in comparison to more traditional 2D approaches: (1) being robust under lighting variations and (2) providing more relevant information. In this paper we present a new 3D facial model based on the curvature properties of the surface. Our system is able to detect the subset of the characteristics of the face with higher discrimination power from a large set. The robustness of the model is tested by comparing recognition rates using both controlled and noncontrolled environments regarding facial expressions and facial rotations. The difference between the recognition rates of the two environments of only 5% proves that the model has a high degree of robustness against pose and facial expressions. We consider that this robustness is enough to implement facial recognition applications, which can achieve up to 91% correct recognition rate. A publish 3D face database containing face rotations and expressions has been created to achieve the recognition experiments.

Keywords:

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