Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Besl, P.J., McKay, N.D.: A Method for Registration of 3-D Shapes. IEEE Trans. on Pattern Analysis and Machine Intelligence 14, 239–256 (1992)
Savran, A., Alyüz, N., Dibeklioğlu, H., Çeliktutan, O., Gökberk, B., Sankur, B., Akarun, L.: Bosphorus Database for 3D Face Analysis. In: Schouten, B., Juul, N.C., Drygajlo, A., Tistarelli, M. (eds.) BIOID 2008. LNCS, vol. 5372, pp. 47–56. Springer, Heidelberg (2008)
Çeliktutan, O., Çinar, H., Sankur, B.: Automatic Facial Feature Extraction Robust Against Facial Expressions and Pose Variations. In: IEEE Int. Conf. on Automatic Face and Gesture Recognition, Amsterdam, Holland (September 2008)
Dibekliolu, H., Salah, A., Akarun, L.: 3D Facial Landmarking Under Expression, Pose, and Occlusion Variations. In: IEEE 2nd International Conferance on Biometrics: Theory, Applications, and Systems (IEEE BTAS), Washington, DC, USA (September 2008)
Gorkberk, B., Savran, A., Ali, A., Akarun, L., Sankur, B.: 3D Face Recognition Benchmarks on the Bosphorus Database with Focus on Facial Expressions. In: Schouten, B., Juul, N.C., Drygajlo, A., Tistarelli, M. (eds.) BIOID 2008. LNCS, vol. 5372, pp. 57–66. Springer, Heidelberg (2008)
Lindeberg, T.: Feature Detection with Automatic Scale Selection. International Journal of Computer Vision 30(2), 77–116 (1998)
Pauly, M., Keiser, R., Gross, M.: Multi-scale Feature Extraction on Point-sampled Surfaces. In: Proceedings of Eurographics 2003, vol. 22(3) (2003)
Witkin, A.: A Scale Space Filtering. In: Proc. 8th Int. Joint Conference on Artificial Intelligence (1983)
Olver, P.J.: Joint Invariants Signatures. Found. Comput. Math. 1, 3–67 (2001)
Cadoni, M., Bicego, M., Grosso, E.: 3D Face Recognition Using Joint Differential Invariants. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 11–25. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cadoni, M., Grosso, E., Lagorio, A., Tistarelli, M. (2011). From 3D Faces to Biometric Identities. In: Vielhauer, C., Dittmann, J., Drygajlo, A., Juul, N.C., Fairhurst, M.C. (eds) Biometrics and ID Management. BioID 2011. Lecture Notes in Computer Science, vol 6583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19530-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-19530-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19529-7
Online ISBN: 978-3-642-19530-3
eBook Packages: Computer ScienceComputer Science (R0)