HIERARCHICAL EVALUATION MODEL FOR 3D FACE RECOGNITION

Sídnei A. Drovetto Jr., Luciano Silva, Olga R. P. Bellon

2008

Abstract

In this paper we propose to perform 3D face matching based on alignments obtained using Simulated Annealing (SA) algorithm guided by the Mean Squared Error (MSE) with M-estimator Sample Consensus (MSAC) and the Surface Interpenetration Measure (SIM). The matching score is obtained by calculation of the SIM after the registration process. Since the SIM is a sensitive measure, it needs a good alignment to give relevance to its value. Our registration approach tends to reach a near global solution and, therefore, produces the necessary precise alignments. By analyzing the matching score, the system can identify if the input images come from the same subject or not. In a verification scenario, we use a hierarchical evaluation model which maximizes the results and reduces the computing time. Extensive experiments were performed on the well-known Face Recognition Grand Challenge (FRGC) v2.0 3D face database using five different facial regions: three regions of the nose; the region of the eyes; and the face itself. Compared to state-of-the-art works, our approach has achieved a high rank-one recognition rate and a high verification rate.

References

  1. Bellon, O. R. P., Silva, L., Queirolo, C., Drovetto, S., and Pamplona, M. (2006). 3d face image registration for face matching guided by the surface interpenetration measure. In ICIP, pages 2661-2664. IEEE.
  2. Bellon, O. R. P., Silva, L., and Queirolo, C. C. (2005). 3D face matching using the surface interpenetration measure. In Lecture Notes in Computer Science, volume 3617, pages 1051-1058. Springer-Verlag.
  3. Besl, P. J. and McKay, N. D. (1992). A method for registration of 3-D shapes. IEEE Trans. Pattern Analysis and Machine Intelligence, 14(2):239-256.
  4. Chang, K. I., Bowyer, K. W., and Flynn, P. J. (2005). Adaptive rigid multi-region selection for handling expression variation in 3D face recognition. In Proc. of IEEE Conf. CVPR, volume 3, pages 157-164.
  5. Chang, K. I., Bowyer, K. W., and Flynn, P. J. (2006). Multiple nose region matching for 3d face recognition under varying facial expression. IEEE Trans. Pattern Analysis and Machine Intelligence, 28(10):1695-1700.
  6. Chen, Y. and Medioni, G. (1992). Object modelling by registration of multiple range images. Image Vision Computing, 10(3):145-155.
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Paper Citation


in Harvard Style

A. Drovetto Jr. S., Silva L. and R. P. Bellon O. (2008). HIERARCHICAL EVALUATION MODEL FOR 3D FACE RECOGNITION . In Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008) ISBN 978-989-8111-21-0, pages 67-74. DOI: 10.5220/0001087900670074


in Bibtex Style

@conference{visapp08,
author={Sídnei A. Drovetto Jr. and Luciano Silva and Olga R. P. Bellon},
title={HIERARCHICAL EVALUATION MODEL FOR 3D FACE RECOGNITION},
booktitle={Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)},
year={2008},
pages={67-74},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001087900670074},
isbn={978-989-8111-21-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2008)
TI - HIERARCHICAL EVALUATION MODEL FOR 3D FACE RECOGNITION
SN - 978-989-8111-21-0
AU - A. Drovetto Jr. S.
AU - Silva L.
AU - R. P. Bellon O.
PY - 2008
SP - 67
EP - 74
DO - 10.5220/0001087900670074