A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Bosphorus Database for 3D Face Analysis
Biometrics and Identity Management
3D Face Recognition Using Joint Differential Invariants
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
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The recognition of human faces, in presence of pose and illumination variations, is intrinsically an ill-posed problem. The direct measurement of the shape for the face surface is now a feasible solution to overcome this problem and make it well-posed. This paper proposes a completely automatic algorithm for face registration and matching. The algorithm is based on the extraction of stable 3D facial features characterizing the face and the subsequent construction of a signature manifold. The facial features are extracted by performing a continuous-to-discrete scale-space analysis. Registration is driven from the matching of triplets of feature points and the registration error is computed as shape matching score. A major advantage of the proposed method is that no data pre-processing is required. Therefore all presented results have been obtained exclusively from the raw data available from the 3D acquisition device. Despite of the high dimensionality of the data (sets of 3D points, possibly with the associate texture), the signature and hence the template generated is very small. Therefore, the management of the biometric data associated to the user data, not only is very robust to environmental changes, but it is also very compact. This reduces the required storage and processing resources required to perform the identification. The method has been tested against the Bosphorus 3D face database and the performances compared to the ICP baseline algorithm. Even in presence of noise in the data, the algorithm proved to be very robust and reported identification performances in line with the current state of the art.