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A novel integral and SE(3)-invariant description for 3D face recognition

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Abstract

We propose a novel description of 3D faces for the task of face recognition. It is based on an integral approach to avoid the errors caused in the case of derivative methods. The geodesic distance computing, which is considered as an integral approach, is performed. The geodesic three-polar parameterization is, firstly, implemented on 3D faces to remove the dependence with regard to the original mesh which is known to be a difficult problem in the 3D data analysis context. Then, the geodesic distances between the pairwise points of the three-polar parameterization are computed to form the Geodesic Distance Matrix description. Intensive experimentations are performed on the BU-3DFE and the Bosphorus face databases. The obtained results showed the accuracy of the proposed integral method for the face description relatively to the identification and the verification protocols. Very competitive rates with the state of the art methods are, also, obtained.

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References

  1. Jribi, M., Rihani, A., Khlifa, A., Ghorbel, F.: An SE(3) invariant description for 3D face recognition. Image Vis. Comput. 89, 106–119 (2019)

    Article  Google Scholar 

  2. Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of the Conference on Computer Vision and Pattern Recognition, pp. 586–591 (1991)

  3. Bai, X.M., Yin, B.C, Shi, Q., Sun, Y.F.: Face recognition using extended fisherface with 3D morphable model. In: Proceedings of the International Conference on Machine Learning and Cybernetics, Guangzhou, pp. 18–21 (2005)

  4. Pan, G., Wu, Z., Pan, Y.: Automatic 3D face verification from range data. In: proceedings of the International Conference on Multimedia and Expo (2003)

  5. Smeets, D., Hermans, J., Vandermeulen, D., Suetens, P.: Isometric deformation invariant 3D shape recognition. Pattern Recognit. 45(7), 2817–2831 (2012)

    Article  MATH  Google Scholar 

  6. Gordon, G.G.: Face recognition based on depth maps and surface curvature. Geometr. Methods Comput. Vis. 1570, 234–247 (1991)

    Article  MATH  Google Scholar 

  7. Li, H., Huang, D., Morvan, J.M., Wang, Y., Chen, L.: Towards 3D face recognition in the real: a registration-free approach using fine-grained matching of 3D keypoint descriptors. Int. J. Comput. Vis. 113, 128–142 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  8. Jribi, M., Mathlouthi, S., Ghorbel, F.: A geodesic multipolar parameterization-based representation for 3D face recognition. Signal Process. Image Commun. 99, 116–464 (2021)

    Article  MATH  Google Scholar 

  9. Gilani, S.Z., Mian, A., Eastwood, P.: Deep, dense and accurate 3D face correspondence for generating population specific deformable models. Pattern Recognit. 69, 238–250 (2017)

    Article  MATH  Google Scholar 

  10. Lin, S., Liu, F., Liu, Y., Shen, L.: Local feature tensor based deep learning for 3D face recognition. In: Proceedings of the International Conference on Automatic Face and Gesture Recognition, pp. 1–5 (2019)

  11. Savran, A., Alyuz, N., Dibeklioglu, H., Celiktutan, O., Gokberk,B., Sankur, B., Akarun, L.: Bosphorus database for 3D face analysis. In: Proceedings of the European Workshop on Biometrics and Identity Management, pp. 47–56 (2008)

  12. Berretti, S., Del Bimbo, A., Pala, P.: 3D face recognition using isogeodesic stripes. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2162–2177 (2010)

    Article  MATH  Google Scholar 

  13. Wang, Y., Liu, J., Tang, X.: Robust 3D face recognition by local shape difference boosting. IEEE Trans. Pattern Anal. Mach. Intell. 32(10), 1858–1870 (2010)

    Article  MATH  Google Scholar 

  14. Kim, D., Hernandez, M., Choi, J., Medioni, G.: Deep 3D face identification. In: Proceedings of the international Joint Conference on Biometrics, pp. 133–142 (2017)

  15. Yin, L., Wei, X., Sun, Y., Wang, J., JRosato, M.: A 3D facial expression database for facial behavior research. In: Proceedings of the International Conference on Automatic Face and Gesture Recognition (2006)

  16. Lei, Y., Guo, Y., Hayat, M., Bennamoun, M., Zhou, X.: A two-phase weighted collaborative representation for 3D partial face recognition with single sample. In: Pattern Recognition, pp. 218–237 (2016)

  17. Cai, Y., Lei, Y., Yang, M., You, Z., Shan, S.: A fast and robust 3D face recognition approach based on deeply learned face representation. Neurocomputing 363, 375–397 (2019)

    Article  MATH  Google Scholar 

  18. Ocegueda, O., Passalis, G., Theoharis, T., Shah, S.K., Kakadiaris, I.A.: UR3D-C: linear dimensionality reduction for efficient 3D face recognition. In: Proceedings of the International Joint Conference on Biometrics, pp. 1–6 (2011)

  19. Szeptycki, P., Ardabilian, M., Chen, L.: A coarse-to-fine curvature analysis-based rotation invariant 3D face landmarking. In: Proceedings of the International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6 (2009)

  20. Bolle, R.M., Connell, J.H., Pankanti, S., Ratha, N.K., Senior, A.W.: The relation between the ROC curve and the CMC. In: Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID’05), 2005, pp. 15–20 (2005)

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Authors and Affiliations

  1. Cristal Laboratory, Grift Research Group, Ecole Nationale des Sciences de l’Informatique (ENSI), La Manouba University, 2010, La Manouba, Tunisia

    Majdi Jribi, Zeineb Othmeni & Faouzi Ghorbel

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  1. Majdi Jribi
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  2. Zeineb Othmeni
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  3. Faouzi Ghorbel
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Contributions

MJ and FG designed the algorithm. ZO implemented the proposed algorithm and made the experimentation. MJ and FG supervised the project. All the authors contributed in the paper redaction.

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Correspondence to Majdi Jribi.

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Jribi, M., Othmeni, Z. & Ghorbel, F. A novel integral and SE(3)-invariant description for 3D face recognition. SIViP 19, 238 (2025). https://doi.org/10.1007/s11760-025-03824-2

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  • DOI: https://doi.org/10.1007/s11760-025-03824-2

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