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Link to original content: https://doi.org/10.1007/s10851-010-0252-0
Three-Dimensional Occlusion Detection and Restoration of Partially Occluded Faces | Journal of Mathematical Imaging and Vision Skip to main content
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Three-Dimensional Occlusion Detection and Restoration of Partially Occluded Faces

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Abstract

This paper presents an innovative three dimensional occlusion detection and restoration strategy for the recognition of three dimensional faces partially occluded by unforeseen, extraneous objects. The detection method considers occlusions as local deformations of the face that correspond to perturbations in a space designed to represent non-occluded faces. Once detected, occlusions represent missing information, or “holes” in the faces. The restoration module exploits the information provided by the non-occluded part of the face to recover the whole face, using an appropriate basis for the space in which non-occluded faces lie. The restoration strategy does not depend on the method used to detect occlusions and can also be applied to restore faces in the presence of noise and missing pixels due to acquisition inaccuracies. The strategy has been experimented on the occluded acquisitions taken from the Bosphorus 3D face database. A method for the generation of real-looking occlusions is also presented. Artificial occlusions, applied to the UND database, allowed for an in-depth analysis of the capabilities of our approach. Experimental results demonstrate the robustness and feasibility of our approach.

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References

  1. Achermann, B., Bunke, H.: Classifying range images of human faces with Hausdorff distance. In: Proc. 15th Int’l Conf. Pattern Recognition, pp. 809–813 (2000)

    Chapter  Google Scholar 

  2. Alyüz, N., Gökberk, B., Akarun, L.: A 3d face recognition system for expression and occlusion invariance. In: Proceedings of 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems, pp. 1–7 (2008)

    Chapter  Google Scholar 

  3. Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Article  Google Scholar 

  4. Berretti, S., Del Bimbo, A., Pala, P.: Description and retrieval of 3d face models using iso-geodesic stripes. In: Proc. 8th ACM Int’l Workshop Multimedia information Retrieval, pp. 13–22 (2006)

    Chapter  Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. Beumier, C., Acheroy, M.: Automatic 3d face authentication. Image Vis. Comput. 18(4), 315–321 (2000)

    Article  Google Scholar 

  7. Blanz, V., Vetter, T.: Face recognition based on fitting a 3d morphable model. IEEE Trans. Pattern Anal. Mach. Intell. 25(9), 1063–1074 (2003)

    Article  Google Scholar 

  8. Bowyer, K.W., Chang, K.I., Flynn, P.J.: A survey of approaches and challenges in 3d and multi-modal 3d + 2d face recognition. Comput. Vis. Image Underst. 101(1), 1–15 (2006)

    Article  Google Scholar 

  9. Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Expression-invariant 3d face recognition. In: Proceedings of Audio- and Video-Based Biometric Person Authentication, pp. 62–70 (2004)

    Google Scholar 

  10. Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Efficient computation of isometry-invariant distances between surfaces. SIAM J. Sci. Comput. 28(5), 1812–1836 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  11. Bronstein, M.M., Bronstein, A.M., Kimmel, R.: Three-dimensional face recognition. Int. J. Comput. Vis. 64(1), 5–30 (2005)

    Article  Google Scholar 

  12. Cartoux, J.Y., Lapreste, J.T., Richetin, M.: Face authentification or recognition by profile extraction from range images. In: Proc. IEEE CS Workshop Interpretation of 3D Scenes, pp. 194–199 (1989)

    Google Scholar 

  13. Chang, K.I., Bowyer, K.W., Flynn, P.J.: Face recognition using 2d and 3d facial data. In: ACM Workshop on Multimodal User Application, pp. 25–32 (2003)

    Google Scholar 

  14. Chang, K.I., Bowyer, K.W., Flynn, P.J.: An evaluation of multimodal 2d + 3d face biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 27(4), 619–624 (2005)

    Article  Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. Colombo, A., Cusano, C., Schettini, R.: A 3d face recognition system using curvature-based detection and holistic multimodal classification. In: Proc. 4th Int’l Symp. on Image and Signal Processing and Analysis, pp. 179–184 (2005)

    Google Scholar 

  17. Colombo, A., Cusano, C., Schettini, R.: 3d face detection using curvature analysis. Pattern Recognit. 39(3), 444–455 (2006)

    Article  MATH  Google Scholar 

  18. Colombo, A., Cusano, C., Schettini, R.: Detection and restoration of occlusions for 3D face recognition. In: Proc. of IEEE International Conference on Multimedia & Expo, pp. 1541–1544 (2006)

    Chapter  Google Scholar 

  19. Colombo, A., Cusano, C., Schettini, R.: Face3 a 2d+3d robust face recognition system. In: Proceedings of 14th IEEE International Conference on Image Analysis and Processing, pp. 393–398 (2007)

    Google Scholar 

  20. Colombo, A., Cusano, C., Schettini, R.: Gappy pca classification for occlusion tolerant 3d face detection. J. Math. Imaging Vis. 35(3), 193–207 (2009)

    Article  MathSciNet  Google Scholar 

  21. De Smet, M., Franses, R., Van Gool, L.: A generalized em approach for 3d model based face recognition under occlusions. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1423–1430 (2006)

    Google Scholar 

  22. Ekenel, H.K., Stiefelhagen, R.: Why is facial occlusion a challenging problem. In: Advances in Biometrics, pp. 299–308 (2009)

    Chapter  Google Scholar 

  23. Everson, R., Sirovich, L.: Karhunen-Loève procedure for gappy data. J. Opt. Soc. Am. A 12(8), 1657–1664 (1995)

    Article  Google Scholar 

  24. Flynn, P.J., Bowyer, K.W., Phillips, P.J.: Assessment of time dependency in face recognition: an initial study. In: Audio- and Video-Based Biometric Person Authentication, pp. 44–51 (2003)

    Chapter  Google Scholar 

  25. Gökberk, B., Akarun, L., Aksan, B.: How to deceive a face recognizer. In: Proceedings of the Biometrics: Challenges arising from Theory to Practice Workshop (2004)

    Google Scholar 

  26. Hwang, B., Lee, S.: Reconstruction of partially damaged face images based on a morphable face model. IEEE Trans. Pattern Anal. Mach. Intell. 25(3), 365–372 (2003)

    Article  Google Scholar 

  27. Kim, J., Choi, J., Yi, J., Turk, M.: Effective representation using ica for face recognition robust to local distortion and partial occlusion. IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1977–1981 (2005)

    Article  Google Scholar 

  28. Kirby, M., Sirovich, L.: Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Mach. Intell. 12(1), 103–108 (1990)

    Article  Google Scholar 

  29. Lin, D., Tang, X.: Quality-driven face occlusion detection and recovery. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1–7 (2007)

    Google Scholar 

  30. 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)

    Article  Google Scholar 

  31. Martinez, A.M.: Recognition of partially occluded and/or imprecisely localized faces using a probabilistic approach. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 712–717 (2000)

    Google Scholar 

  32. Martinez, A.M.: Recognizing imprecisely localized, partially occluded and expression variant faces from a single sample per class. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 748–763 (2002)

    Article  Google Scholar 

  33. Medioni, G., Waupotitsch, R.R.: Face recognition and modeling in 3d. In: Proc. IEEE Int’l Workshop Analysis and Modeling of Faces and Gestures, pp. 232–233 (2003)

    Google Scholar 

  34. Mo, Z., Lewis, J.P., Neumann, U.: Face inpainting with local linear representations. In: British Machine Vision Conference, vol. 1, pp. 347–356 (2004)

    Google Scholar 

  35. Nagamine, T., Uemura, T., Masuda, I.: 3d facial image analysis for human identification. In: Proc. Int’l Conf. Pattern Recognition, pp. 324–327 (1992)

    Google Scholar 

  36. Park, B., Lee, K., Lee, S.: Face recognition using face-arg matching. IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1982–1988 (2005)

    Article  Google Scholar 

  37. Park, J.S., Oh, Y.H., Ahn, S.C., Lee, S.W.: Glasses removal from facial image using recursive error compensation. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 805–811 (2005)

    Article  Google Scholar 

  38. Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 84–91 (1994)

    Chapter  Google Scholar 

  39. Samir, C., Srivastava, A., Daoudi, M.: Three-dimensional face recognition using shapes of facial curves. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1858–1863 (2006)

    Article  Google Scholar 

  40. Savran, A., Alyüz, N., Dibeklioglu, H., Çeliktutan, O., Gökberk, B., Akarun, L., Sankur, B.: Bosphorus database for 3d face analysis. In: The First COST 2101 Workshop on Biometrics and Identity Management, pp. 47–56 (2008)

    Chapter  Google Scholar 

  41. Skocaj, D., Leonardis, A.: Robust recognition and pose determination of 3-d objects using range images in eigenspace. In: Third International Conference on 3-D Digital Imaging and Modeling, pp. 171–178 (2001)

    Chapter  Google Scholar 

  42. Tarrés, F., Rama, A.: A novel method for face recognition under partial occlusion or facial expression variations. In: Proc. 47th Int’l Symp. ELMAR, pp. 163–166 (2005)

    Chapter  Google Scholar 

  43. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  44. Zhang, W., Shan, S., Chen, X., Gao, W.: Local Gabor binary patterns based on Kullback Óleibler divergence for partially occluded face recognition. IEEE Signal Process. Lett. 14(11), 875–878 (2007)

    Article  Google Scholar 

Download references

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Correspondence to Alessandro Colombo.

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Colombo, A., Cusano, C. & Schettini, R. Three-Dimensional Occlusion Detection and Restoration of Partially Occluded Faces. J Math Imaging Vis 40, 105–119 (2011). https://doi.org/10.1007/s10851-010-0252-0

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