Low-cost Automatic Inpainting for Artifact Suppression in Facial Images

André Sobiecki, Alexandru Telea, Gilson Giraldi, Luiz Antonio Neves, Carlos Eduardo Thomaz


Facial images are often used in applications that need to recognize or identify persons. Many existing facial recognition tools have limitations with respect to facial image quality attributes such as resolution, face position, and artifacts present in the image. In this paper we describe a new low-cost framework for preprocessing low-quality facial images in order to render them suitable for automatic recognition. For this, we first detect artifacts based on the statistical difference between the target image and a set of pre-processed images in the database. Next, we eliminate artifacts by an inpainting method which combines information from the target image and similar images in our database. Our method has low computational cost and is simple to implement, which makes it attractive for usage in low-budget environments. We illustrate our method on several images taken from public surveillance databases, and compare our results with existing inpainting techniques.


  1. AFP (2012). Australian Federal Police, National Missing Persons Coordination Centre. http:// www.missingpersons.gov.au.
  2. Amaral, V. and Thomaz, C. (2008). Normalizac¸a˜o espacial de imagens frontais de face. Dept. of Electrical Engineering, Univ. Center of FEI, Brazil. http://fei.edu.br cet/relatorio tecnico 012008.pdf.
  3. Aptoula, E. and Lefèvre, S. (2008). A comparative study on multivariate mathematical morphology. Pattern Recogn. Lett., 29(2):109-118.
  4. Ayinde, O. and Yang, Y. (2002). Face recognition approach based on rank correlation of Gabor-filtered images. Pattern Recogn., 35(6):1275-1289.
  5. Bertalmio, M., Sapiro, G., and Bertozzi, A. (2001). Navierstokes, fluid dynamics, and image and video inpainting. In Proc. CVPR, pages 355-362.
  6. Bertalmio, M., Sapiro, G., Caselles, V., and Ballester, C. (2000). Image inpainting. In Proc. ACM SIGGRAPH, pages 417-424.
  7. Bugeau, A. and Bertalmio, M. (2009). Combining texture synthesis and diffusion for image inpainting. In Proc. VISAPP, pages 26-33.
  8. Bugeau, A., Bertalmio, M., Caselles, V., and Sapiro, G. (2009). A unifying framework for image inpainting. Technical report, Intitute for Math. and Applications (IMA). http://www.ima.umn.edu.
  9. Bussab, W. and Morrettin, P. (2002). Estátistica Básica. Editora Saraiva, Sa˜o Paulo, Brazil.
  10. Castillo, O. (2006). Survey about facial image quality. Technical report, Fraunhofer IGD, TU Darmstadt.
  11. Chan, T. and Shen, J. (2000a). Mathematical model for local deterministic inpaintings. In Tech. Report CAM 00-11. Image Processing Group, UCLA.
  12. Chan, T. and Shen, J. (2000b). Non-texture inpainting by curvature driven diffusions. In Tech. Report CAM 00- 35. Image Processing Group, UCLA.
  13. Chou, J., Yang, C., and Gong, S. (2012). Face-off: Automatic alteration of facial features. Multimedia Tools and Applications, 56(3):569-596.
  14. Farbman, Z., Hoffer, G., Lipman, Y., CohenOr, D., and Lischinski, D. (2009). Coordinates for instant image cloning. In Proc. ACM SIGGRAPH, pages 342-450.
  15. FGB (2012). Federal Goverment of Brazil, Justice Ministry - Missing Persons. http:// www.desaparecidos.mj.gov.br.
  16. Hjelmas, E. and Low, B. (2001). Face detection: A survey. CVIU, 83(3):236-274.
  17. ISO (2004). Final draft international standard of biometric data interchange formats (part 5, face image data, ISO-19794-5 FDIS).
  18. Jeschke, S., Cline, D., and Wonka, P. (2009). A GPU laplacian solver for diffusion curves and poisson image editing. In Proc. ACM SIGGRAPH Asia, pages 1-8.
  19. Joshi, N., Matusik, W., Adelson, E., Csail, M., and Kriegman, D. (2010). Personal photo enhancement using example images. In Proc. ACM SIGGRAPH, pages 1-15.
  20. Li, H., Wang, S., Zhang, W., and Wu, M. (2010). Image inpainting based on scene transform and color transfer. Pattern. Recogn. Lett., 31(7):582-592.
  21. M.J. Lyons, S. Akamatsu, M. K. and Gyoba, J. (1998). Coding facial expressions with gabor wavelets. In Proc. FG, pages 200-204. IEEE.
  22. MPO (2012). Missing People from United Kingdom. http://www.missingpeople.org.uk.
  23. Oliveira, M. M., Bowen, B., Mckenna, R., and Chang, Y. (2001). Fast digital image inpainting. In Proc. VIIP, pages 261-266.
  24. Pérez, P., Gangnet, M., and Blake, A. (2003). Poisson image editing. In Proc. ACM SIGGRAPH, pages 313- 318.
  25. P.J. Phillips, M. Hyeonjoon, S. R. and Rauss, P. (2000). The FERET evaluation methodology for face-recognition algorithms. IEEE TPAMI, 22(10):1090-1104.
  26. Sellahewa, H. and Jassim, S. (2010). Image-quality-based adaptive face recognition. IEEE TIM, 59(4):805-813.
  27. Sethian, J. (1999). Level Set Methods and Fast Marching Methods. Cambridge Univ. Press, 2nd edition.
  28. Spiegel, M. and Stephens, L. (2008). Statistics. Schaum's Outlines.
  29. Telea, A. (2004). An image inpainting technique based on the fast marching method. J. Graphics. Tools, 9(1):23-34.
  30. Thakare, N. and Thakare, V. (2012). Biometrics standards and face image format for data interchange - a review. Int. J. of Advances in Engineering and Technology, 2(1):385-392.
  31. Thomaz, C. and Giraldi, G. (2010). A new ranking method for principal components analysis and its application to face image analysis. Image Vision Comput., 28(6):902-913.
  32. Wang, Z., Lu, L., and Bovik, A. (2004). Video quality assessment based on structural distortion measurement. Sig. Proc.: Image Comm., 19(2):121-132.
  33. Zamani, A., Awang, M., Omar, N., and Nazeer, S. (2008). Image quality assessments and restoration for face detection and recognition system images. In Proc. AMS, pages 505-510.
  34. Zhao, W., Chellappa, R., Phillips, P., and Rosenfeld, A. (2003). Face recognition: A literature survey. ACM Computing Surveys, 35(4):399-458.

Paper Citation

in Harvard Style

Sobiecki A., Telea A., Giraldi G., Neves L. and Thomaz C. (2013). Low-cost Automatic Inpainting for Artifact Suppression in Facial Images . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 41-50. DOI: 10.5220/0004212300410050

in Bibtex Style

author={André Sobiecki and Alexandru Telea and Gilson Giraldi and Luiz Antonio Neves and Carlos Eduardo Thomaz},
title={Low-cost Automatic Inpainting for Artifact Suppression in Facial Images},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},

in EndNote Style

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - Low-cost Automatic Inpainting for Artifact Suppression in Facial Images
SN - 978-989-8565-47-1
AU - Sobiecki A.
AU - Telea A.
AU - Giraldi G.
AU - Neves L.
AU - Thomaz C.
PY - 2013
SP - 41
EP - 50
DO - 10.5220/0004212300410050