Advertisement

Video-Based Facial Kinship Verification

  • Haibin YanEmail author
  • Jiwen Lu
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

In this chapter, we investigate the problem of video-based kinship verification via human face analysis. While several attempts have been made on facial kinship verification from still images, to our best knowledge, the problem of video-based kinship verification has not been formally addressed in the literature. In this chapter, we first present a new video face dataset called Kinship Face Videos in the Wild (KFVW) which were captured in wild conditions for the video-based kinship verification study, as well as the standard benchmark. Then, we employ our benchmark to evaluate and compare the performance of several state-of-the-art metric learning-based kinship verification methods. Finally, several observations are provided in evaluation part which may give some hints for the future direction for video-based kinship verification studies.

Keywords

Face Image Local Binary Pattern Equal Error Rate Multiple Kernel Learning Local Binary Pattern Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)CrossRefzbMATHGoogle Scholar
  2. 2.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)Google Scholar
  3. 3.
    Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: International Conference on Machine Learning, pp. 209–216 (2007)Google Scholar
  4. 4.
    Dibeklioglu, H., Salah, A.A., Gevers, T.: Like father, like son: facial expression dynamics for kinship verification. In: IEEE International Conference on Computer Vision, pp. 1497–1504 (2013)Google Scholar
  5. 5.
    Du, S., Ward, R.K.: Improved face representation by nonuniform multilevel selection of gabor convolution features. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 39(6), 1408–1419 (2009)CrossRefGoogle Scholar
  6. 6.
    Fang, R., Gallagher, A.C., Chen, T., Loui, A.: Kinship classification by modeling facial feature heredity. In: IEEE International Conference on Image Processing, pp. 2983–2987 (2013)Google Scholar
  7. 7.
    Fang, R., Tang, K.D., Snavely, N., Chen, T.: Towards computational models of kinship verification. In: IEEE International Conference on Image Processing, pp. 1577–1580 (2010)Google Scholar
  8. 8.
    Guo, G., Wang, X.: Kinship measurement on salient facial features. IEEE Trans. Instrum. Meas. 61(8), 2322–2325 (2012)CrossRefGoogle Scholar
  9. 9.
    Guo, Y., Dibeklioglu, H., van der Maaten, L.: Graph-based kinship recognition. In: International Conference on Pattern Recognition, pp. 4287–4292 (2014)Google Scholar
  10. 10.
    Kan, M., Shan, S., Xu, D., Chen, X.: Side-information based linear discriminant analysis for face recognition. In: British Machine Vision Conference, pp. 1–12 (2011)Google Scholar
  11. 11.
    Kohli, N., Singh, R., Vatsa, M.: Self-similarity representation of weber faces for kinship classification. In: IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp. 245–250 (2012)Google Scholar
  12. 12.
    Köstinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2288–2295 (2012)Google Scholar
  13. 13.
    Lu, J., Zhou, X., Tan, Y.P., Shang, Y., Zhou, J.: Neighborhood repulsed metric learning for kinship verification. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 331–345 (2014)Google Scholar
  14. 14.
    Nguyen, H.V., Bai, L.: Cosine similarity metric learning for face verification. In: Asian Conference on Computer Vision, pp. 709–720 (2010)Google Scholar
  15. 15.
    Qin, X., Tan, X., Chen, S.: Tri-subject kinship verification: understanding the core of a family. IEEE Trans. Multimed. 17(10), 1855–1867 (2015)CrossRefGoogle Scholar
  16. 16.
    Shao, M., Xia, S., Fu, Y.: Genealogical face recognition based on UB kinface database. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 60–65 (2011)Google Scholar
  17. 17.
    Somanath, G., Kambhamettu, C.: Can faces verify blood-relations? In: IEEE International Conference on Biometrics: Theory, Applications and Systems, pp. 105–112 (2012)Google Scholar
  18. 18.
    Xia, S., Shao, M., Fu, Y.: Kinship verification through transfer learning. In: International Joint Conference on Artificial Intelligence, pp. 2539–2544 (2011)Google Scholar
  19. 19.
    Xia, S., Shao, M., Fu, Y.: Toward kinship verification using visual attributes. In: International Conference on Pattern Recognition, pp. 549–552 (2012)Google Scholar
  20. 20.
    Xia, S., Shao, M., Luo, J., Fu, Y.: Understanding kin relationships in a photo. IEEE Trans. Multimed. 14(4), 1046–1056 (2012)CrossRefGoogle Scholar
  21. 21.
    Yan, H., Hu, J.: Video-based kinship verification using distance metric learning. Pattern Recognition (2017)Google Scholar
  22. 22.
    Yan, H., Lu, J., Deng, W., Zhou, X.: Discriminative multimetric learning for kinship verification. IEEE Trans. Inf. Forensics Secur. 9(7), 1169–1178 (2014)CrossRefGoogle Scholar
  23. 23.
    Yan, H., Lu, J., Zhou, X.: Prototype-based discriminative feature learning for kinship verification. IEEE Trans. Cybern. 45(11), 2535–2545 (2015)CrossRefGoogle Scholar
  24. 24.
    Zhou, X., Hu, J., Lu, J., Shang, Y., Guan, Y.: Kinship verification from facial images under uncontrolled conditions. In: ACM International Conference on Multimedia, pp. 953–956 (2011)Google Scholar
  25. 25.
    Zhou, X., Lu, J., Hu, J., Shang, Y.: Gabor-based gradient orientation pyramid for kinship verification under uncontrolled environments. In: ACM International Conference on Multimedia, pp. 725–728 (2012)Google Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Tsinghua UniversityBeijingChina

Personalised recommendations