Abstract.A new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions is presented in this paper. This database is unique from three aspects: i) the facial expressions are composed of judiciously selected subset of Action Units as well as the six basic emotions, and many actors/actresses are incorporated to obtain more realistic expression data; ii) a rich set of head pose variations are available; and iii) different types of face occlusions are included. Hence, this new database can be a very valuable resource for development and evaluation of algorithms on face recognition under adverse conditions and facial expression analysis as well as for facial expression synthesis.
Biometric identification from three-dimensional (3-D) facial surface characteristics has become popular, especially in high security applications. In this paper, we propose a fully automatic expression insensitive 3-D face recognition system. Surface deformations due to facial expressions are a major problem in 3-D face recognition. The proposed approach deals with such challenging conditions in several aspects. First, we employ a fast and accurate region-based registration scheme that uses common region models. These common models make it possible to establish correspondence to all the gallery samples in a single registration pass. Second, we utilize curvature-based 3-D shape descriptors. Last, we apply statistical feature extraction methods. Since all the 3-D facial features are regionally registered to the same generic facial component, subspace construction techniques may be employed. We show that linear discriminant analysis significantly boosts the identification accuracy. We demonstrate the recognition ability of our system using the multiexpression Bosphorus and the most commonly used 3-D face database, Face Recognition Grand Challenge (FRGCv2). Our experimental results show that in both databases we obtain comparable performance to the best rank-1 correct classification rates reported in the literature so far: 98.19% for the Bosphorus and 97.51% for the FRGCv2 database. We have also carried out the standard receiver operating characteristics (ROC III) experiment for the FRGCv2 database. At an FAR of 0.1%, the verification performance was 86.09%. This shows that model-based registration is beneficial in identification scenarios where speed-up is important, whereas for verification one-to-one registration can be more beneficial.
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