Abstract
In this paper we present a method for 3D face recognition that is suitable for verification systems. A Simulated Annealing (SA)-based approach for range image registration is used to perform 3D face matching. The Surface Interpenetration Measure (SIM) is used during the registration process to assess precise alignments. This measure is then used as similarity score between two face images. In the verification scenario, we propose a hierarchical evaluation model to answer if two face images belong or not to the same subject. Initially the face image is segmented into four different regions, which are hierarchically compared according. The hierarchy is defined according to each region’s size, arranged from the smallest ones to the biggest ones. At each level of the hierarchy, the similarity measure is evaluated to verify if at that step we can ensure if both faces are from the same subject. With this approach, we can boost the system performance and also reduce its computational time. Experimental results were performed using all images from the FRGC v2 database, and the results show the effectiveness of this approach.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Cook, J., McCool, C., Chandran, V., Sridharan, S.: Combined 2D/3D face recognition using log-gabor templates. In: Proc. IEEE Int’l Conf. Video and Signal Based Surveillance, vol. 83 (2006)
Husken, M., Brauckmann, M., Gehlen, S., der Malsburg, C.V.: Strategies and benefits of fusion of 2D and 3D face recognition. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, p. 174. IEEE Computer Society, Los Alamitos (2005)
Kakadiaris, I., Passalis, G., Toderici, G., Murtuza, N., Theoharis, T.: Three-dimensional face recognition in the presence of facial expression: An annotated deformable model approach 29(4), 640–649 (2007)
Lu, X., Jain, A.K., Colbry, D.: Matching 2.5D face scans to 3D models. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 31–43 (2006)
Chang, K.I., Bowyer, K.W., Flynn, P.J.: Multiple nose region matching for 3D face recognition under varying facial expression. IEEE Trans. Pattern Anal. Mach. Intell. 28(10), 1695–1700 (2006)
Faltemier, T., Bowyer, K.W., Flynn, P.J.: A region ensemble for 3d face recognition. IEEE Trans. Inf. Forensics Security 3(1), 62–73 (2008)
Lin, W.Y., Wong, K.C., Boston, N., Hu, Y.H.: 3d face recognition under expression variations using similarity metrics fusion. In: Proc. IEEE Int’l Conf. Multimedia and Expo., pp. 727–730 (2007)
Silva, L., Bellon, O.R.P., Boyer, K.: Robust range image registration using the surface interpenetration measure and enhanced genetic algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 27, 762–776 (2005)
Silva, L., Bellon, O.R.P., Boyer, K.: Robust Range Image Registration Using Genetic Algorithms and the Surface Interpenetration Measure. Machine Perception and Artificial Intelligence, vol. 60. World Scientific Publishing, Singapore (2005)
Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Proc. Int’l Conf. 3-D Digital Imaging and Modeling, pp. 145–152 (2001)
Gelfand, N., Ikemoto, L., Rusinkiewicz, S., Levoy, M.: Geometrically stable sampling for the ICP algorithm. In: Proc. Int’l Conf. 3-D Digital Imaging and Modeling, pp. 260–267 (2003)
Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 947–954 (2005)
Kittler, J., Hatef, M., Duin, R., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)
Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)
Chen, Y., Medioni, G.: Object modelling by registration of multiple range images. Image Vision Computing 10(3), 145–155 (1992)
Bellon, O.R.P., Silva, L., Queirolo, C., Drovetto Jr., S., Segundo, M.P.: 3D face image registration for face matching guided by the surface interpenetration measure. In: Proc. IEEE Int’l Conf. Image Processing, pp. 2661–2664 (2006)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Torr, P., Zisserman, A.: MLESAC: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding 78, 138–156 (2000)
Lundy, M., Mees, A.: Convergence of an annealing algorithm. Mathematical Programming: Series A and B 34(1), 111–124 (1986)
Rayward-Smith, V.J., Osman, I.H., Reeves, C.R., Smith, G.D.: Modern Heuristic Search Methods. John Wiley & Sons Ltd, Chichester (1996)
Queirolo, C., Segundo, M.P., Bellon, O.R.P., Silva, L.: Noise versus facial expression on 3D face recognition. In: Proc. Int’l Conf. Image Analysis and Processing, pp. 171–176 (2007)
Segundo, M.P., Queirolo, C., Bellon, O.R.P., Silva, L.: Automatic 3d facial segmentation and landmark detection. In: Proc. Int’l Conf. Image Analysis and Processing, pp. 431–436 (2007)
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
Queirolo, C.C., Drovetto, S.A., Silva, L., Bellon, O.R.P., Segundo, M.P. (2009). Hierarchical Evaluation Model: Extended Analysis for 3D Face Recognition. In: Ranchordas, A., Araújo, H.J., Pereira, J.M., Braz, J. (eds) Computer Vision and Computer Graphics. Theory and Applications. VISIGRAPP 2008. Communications in Computer and Information Science, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10226-4_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-10226-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10225-7
Online ISBN: 978-3-642-10226-4
eBook Packages: Computer ScienceComputer Science (R0)