Pose and expression-invariant 3d face recogntion using elastic radial curves


In this paper we explore the use of shapes of elastic radial curves to model 3D facial deformations, caused by changes in facial expressions. We represent facial surfaces by indexed collections of radial curves on them, emanating from the nose tips, and com-pare the facial shapes by comparing the shapes of their corresponding curves. Using a past approach on elastic shape analysis of curves, we obtain an algorithm for comparing facial surfaces. We also introduce a quality control module which allows our approach to be robust to pose variation and missing data. Comparative evaluation using a com-mon experimental setup on GAVAB dataset, considered as the most expression-rich and noise-prone 3D face dataset, shows that our approach outperforms other state-of-the-art approaches. 1 Introduction and related work A biometric-based recognition system can be very useful in a variety of applications. While some biometric modalities, such as fingerprints and iris, have already reached very high level of accuracy, they have a limited use in non-cooperative scenarios. On the other hand

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