Abstract
We propose a vector representation (called a 3D signature) for 3D face shape in biometrics applications. Elements of the vector correspond to fixed surface points in a face-centered coordinate system. Since the elements are registered to the face, comparisons of vectors to produce match scores can be performed without a probe to gallery alignment step such as an invocation of the iterated closest point (ICP) algorithm in the calculation of each match score. The proposed 3D face recognition method employing the 3D signature ran more than three orders of magnitude faster than a traditional ICP based distance implementation, without sacrificing accuracy. As a result, it is feasible to apply distance based 3D face biometrics to recognition scenarios that, because of computational constraints, may have previously been limited to verification. Our use of more complex shape regions, which is a trivial task with the use of 3D signatures, improves biometric performance over simple spherical cut regions used previously [1]. Experimental results with a large database of 3D images demonstrate the technique and its advantages.
Chapter PDF
Similar content being viewed by others
References
Faltemier, T., Bowyer, K.W., Flynn, P.J.: A Region Ensemble for 3D Face Recognition. IEEE Transactions on Information Forensics and Security 3(1), 62–73 (2008)
Chang, K.I., Bowyer, K.W., Flynn, P.J.: Multiple Nose Region Matching for 3D Face Recognition under varying facial expression. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10), 1695–1700 (2006)
Lu, X., Colbry, D., Jain, A.K.: Three-Dimensional Model Based Face Recognition. In: 17th International Conference on Pattern Recognition (2004)
Russ, T., Boehnen, C., Peters, T.: 3D Face Recognition Using 3D Alignment for PCA. In: Computer Vision and Pattern Recognition, New York, pp. 1391–1398 (2006)
Besl, P., McKay, N.: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 239–256 (1992)
Bentley, J.L.: K-d Trees for Semidynamic Point Sets. In: SCG 1990: Proc. 6th Annual Symposium on Computational Geometry, pp. 187–197 (1990)
Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 947–954 (2005)
Colbry, D., Folarin, O., Stockman, G.: 3D Face Identification—Experiments Towards a Large Gallery. Society of Photographic Instrumentation Engineers Defense & Security (2008)
Kakadiaris, I.A., Passalis, G., Toderici, G., Murtuza, N., Theoharis, T.: 3D Face Recognition. In: Proceedings of the British Machine Vision Conference, pp. 200–208 (2006)
Al-Osaimi, F.R., Bennamoun, M., Mian, A.: Integration of local and global geometrical cues for 3D face recognition. Pattern Recognition Letters, 1030–1040 (2008)
Kittler, J.V., Matas, J., Jonsson, K., Sanchez, M.U.R.: Combining Evidence in Personal Identity Verification Systems. Pattern Recognition Letters, 845–852 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Boehnen, C., Peters, T., Flynn, P.J. (2009). 3D Signatures for Fast 3D Face Recognition. In: Tistarelli, M., Nixon, M.S. (eds) Advances in Biometrics. ICB 2009. Lecture Notes in Computer Science, vol 5558. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01793-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-01793-3_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01792-6
Online ISBN: 978-3-642-01793-3
eBook Packages: Computer ScienceComputer Science (R0)