This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure
February 2010 (vol. 32 no. 2)
pp. 206-219
ASCII Text
x 
 
Chauã C. Queirolo, Luciano Silva, Olga R.P. Bellon, Maurício Pamplona Segundo, "3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 2, pp. 206-219, February, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2009.14,
author = {Chauã C. Queirolo and Luciano Silva and Olga R.P. Bellon and Maurício Pamplona Segundo},
title = {3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {32},
number = {2},
issn = {0162-8828},
year = {2010},
pages = {206-219},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.14},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - 3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure
IS - 2
SN - 0162-8828
SP206
EP219
EPD - 206-219
A1 - Chauã C. Queirolo,
A1 - Luciano Silva,
A1 - Olga R.P. Bellon,
A1 - Maurício Pamplona Segundo,
PY - 2010
KW - 3D face recognition
KW - Surface Interpenetration Measure (SIM)
KW - range image registration.
VL - 32
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Chauã C. Queirolo, Universidade Federal do Parana, Curitiba
Luciano Silva, Universidade Federal do Parana, Curitiba
Olga R.P. Bellon, Universidade Federal do Parana, Curitiba
Maurício Pamplona Segundo, Universidade Federal do Parana, Curitiba
This paper presents a novel automatic framework to perform 3D face recognition. The proposed method uses a Simulated Annealing-based approach (SA) for range image registration with the Surface Interpenetration Measure (SIM), as similarity measure, in order to match two face images. The authentication score is obtained by combining the SIM values corresponding to the matching of four different face regions: circular and elliptical areas around the nose, forehead, and the entire face region. Then, a modified SA approach is proposed taking advantage of invariant face regions to better handle facial expressions. Comprehensive experiments were performed on the FRGC v2 database, the largest available database of 3D face images composed of 4,007 images with different facial expressions. The experiments simulated both verification and identification systems and the results compared to those reported by state-of-the-art works. By using all of the images in the database, a verification rate of 96.5 percent was achieved at a False Acceptance Rate (FAR) of 0.1 percent. In the identification scenario, a rank-one accuracy of 98.4 percent was achieved. To the best of our knowledge, this is the highest rank-one score ever achieved for the FRGC v2 database when compared to results published in the literature.

[1] “IMAGO Research Group: 3D Face Recognition Homepage,” http://www.imago.ufpr.br3D_Face_Recognition , 2009.
[2] A.F. Abate, M. Nappi, D. Riccio, and G. Sabatino, “2D and 3D Face Recognition: A Survey,” Pattern Recognition Letters, vol. 28, no. 14, pp. 1885-1906, 2007.
[3] F. Al Osaimi, M. Bennamoun, and A. Mian, “An Expression Deformation Approach to Non-Rigid 3D Face Recognition,” Int'l J. Computer Vision, vol. 81, pp. 302-316, 2009.
[4] O.R.P. Bellon, L. Silva, C. Queirolo, S. DrovettoJr., and M.P. Segundo, “3D Face Image Registration for Face Matching Guided by the Surface Interpenetration Measure,” Proc. IEEE Int'l Conf. Image Processing, pp. 2661-2664, 2006.
[5] P.J. Besl and N.D. McKay, “A Method for Registration of 3-D Shapes,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239-256, Feb. 1992.
[6] R. Bolle, J. Connell, S. Pankanti, N. Ratha, and A. Senior, Guide to Biometrics. Springer Verlag, 2003.
[7] K.W. Bowyer, K. Chang, and P.J. Flynn, “A Survey of Approaches and Challenges in 3D and Multi-Modal 3D+2D Face Recognition,” Computer Vision and Image Understanding, vol. 101, pp. 1-15, 2006.
[8] K.I. Chang, K.W. Bowyer, and P.J. Flynn, “Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1695-1700, Oct. 2006.
[9] J. Cook, V. Chandran, S. Sridharan, and C. Fookes, “Face Recognition from 3D Data Using Iterative Closest Point Algorithm and Gaussian Mixture Models,” Proc. Int'l Symp. 3D Data Processing, Visualization, and Transmission, pp. 502-509, 2004.
[10] J. Cook, C. McCool, V. Chandran, and S. Sridharan, “Combined 2D/3D Face Recognition Using Log-Gabor Templates,” Proc. IEEE Int'l Conf. Video and Signal Based Surveillance, p. 83, 2006.
[11] G. Dalley and P.J. Flynn, “Range Image Registration: A Software Platform and Empirical Evaluation,” Proc. Int'l Conf. 3-D Digital Imaging and Modeling, pp. 246-253, 2001.
[12] S. DrovettoJr., L. Silva, and O.R.P. Bellon, “Hierarchical Evaluation Model for 3D Face Recognition,” Proc. Int'l Conf. Computer Vision Theory and Applications, vol. 2, pp. 67-74, 2008.
[13] T. Faltemier, K. Bowyer, and P. Flynn, “Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition,” Proc. Int'l Conf. Biometrics: Theory, Applications, and Systems, vol. 1, pp. 1-6, 2007.
[14] T. Faltemier, K.W. Bowyer, and P.J. Flynn, “A Region Ensemble for 3D Face Recognition,” IEEE Trans. Information Forensics and Security, vol. 3, no. 1, pp. 62-73, Mar. 2008.
[15] P.J. Flynn, T. Faltemier, and K.W. Bowyer, “3D Face Recognition,” Handbook of Biometrics, A.K. Jain, A. Ross, and P.J. Flynn, eds., chapter 11, pp. 211-229, Springer, 2008.
[16] N. Gelfand, L. Ikemoto, S. Rusinkiewicz, and M. Levoy, “Geometrically Stable Sampling for the ICP Algorithm,” Proc. Int'l Conf. 3-D Digital Imaging and Modeling, pp. 260-267, 2003.
[17] J. Huang, B. Heisele, and V. Blanz, “Component-Based Face Recognition with 3D Morphable Models,” Proc. Int'l Conf. Audio and Video-Based Biometric Person Authentication, pp. 27-34, 2003.
[18] M. Husken, M. Brauckmann, S. Gehlen, and C.V. der Malsburg, “Strategies and Benefits of Fusion of 2D and 3D Face Recognition,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp.174-174, 2005.
[19] I. Kakadiaris, G. Passalis, G. Toderici, N. Murtuza, and T. Theoharis, “Three-Dimensional Face Recognition in the Presence of Facial Expression: An Annotated Deformable Model Approach,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 640-649, Apr. 2007.
[20] S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, “Optimization by Simulated Annealing,” Science, vol. 220, no. 4598, pp. 671-680, 1983.
[21] J. Kittler, M. Hatef, R. Duin, and J. Matas, “On Combining Classifiers,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 226-239, Mar. 1998.
[22] W.-Y. Lin, K.-C. Wong, N. Boston, and Y.H. Hu, “3D Face Recognition under Expression Variations Using Similarity Metrics Fusion,” Proc. IEEE Int'l Conf. Multimedia and Expo, pp. 727-730, 2007.
[23] X. Lu and A.K. Jain, “Deformation Modeling for Robust 3D Face Matching,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 1377-1383, 2006.
[24] X. Lu, A.K. Jain, and D. Colbry, “Matching 2.5D Face Scans to 3D Models,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 1, pp. 31-43, Jan. 2006.
[25] M. Lundy and A. Mees, “Convergence of an Annealing Algorithm,” Math. Programming: Series A and B, vol. 34, no. 1, pp. 111-124, 1986.
[26] A. Martinez, “Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample Per Class,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 748-763, June 2002.
[27] T. Maurer, D. Guigonis, I. Maslov, B. Pesenti, A. Tsaregorodtsev, D. West, and G. Medioni, “Performance of Geometrix ActiveID 3D Face Recognition Engine on the FRGC Data,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp.154-154, 2005.
[28] A. Mian, M. Bennamoun, and R. Owens, “An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 11, pp. 1927-1943, June 2007.
[29] P.J. Phillips, P.J. Flynn, T. Scruggs, K.W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, “Overview of the Face Recognition Grand Challenge,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 947-954, 2005.
[30] C. Queirolo, M.P. Segundo, O.R.P. Bellon, and L. Silva, “Noise versus Facial Expression on 3D Face Recognition,” Proc. Int'l Conf. Image Analysis and Processing, pp. 171-176, 2007.
[31] V.J. Rayward-Smith, I.H. Osman, C.R. Reeves, and G.D. Smith, Modern Heuristic Search Methods. John Wiley & Sons Ltd., 1996.
[32] S. Rusinkiewicz and M. Levoy, “Efficient Variants of the ICP Algorithm,” Proc. Int'l Conf. 3-D Digital Imaging and Modeling, pp.145-152, 2001.
[33] M.P. Segundo, C. Queirolo, O.R.P. Bellon, and L. Silva, “Automatic 3D Facial Segmentation and Landmark Detection,” Proc. Int'l Conf. Image Analysis and Processing, pp. 431-436, 2007.
[34] L. Silva, O.R.P. Bellon, P.F.U. Gotardo, and K.L. Boyer, “Range Image Registration Using Enhanced Genetic Algorithms,” Proc. IEEE Int'l Conf. Image Processing, vol. 2, pp. 711-714, 2003.
[35] L. Silva, O.R.P. Bellon, and K. Boyer, Robust Range Image Registration Using Genetic Algorithms and the Surface Interpenetration Measure, vol. 60. World Scientific Publishing, 2005.
[36] L. Silva, O.R.P. Bellon, and K. Boyer, “Robust Range Image Registration Using the Surface Interpenetration Measure and Enhanced Genetic Algorithms,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 762-776, May 2005.
[37] P. Torr and A. Zisserman, “MLESAC: A New Robust Estimator with Application to Estimating Image Geometry,” Computer Vision and Image Understanding, vol. 78, pp. 138-156, 2000.
[38] W. Zhao, R. Chellappa, P.J. Phillips, and A. Rosenfeld, “Face Recognition: A Literature Survey,” ACM Computing Surveys, vol. 35, no. 4, pp. 399-458, 2003.

Index Terms:
3D face recognition, Surface Interpenetration Measure (SIM), range image registration.
Citation:
Chauã C. Queirolo, Luciano Silva, Olga R.P. Bellon, Maurício Pamplona Segundo, "3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 2, pp. 206-219, Feb. 2010, doi:10.1109/TPAMI.2009.14
Usage of this product signifies your acceptance of the Terms of Use.