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Family verification based on similarity of individual family member’s facial segments

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Abstract

Humans process faces to recognize family resemblance and act accordingly. Undoubtedly, they are capable of recognizing their kin and family members. In this paper, we study the facts and valid assumptions of facial resemblance in family members’ facial segments. Our analysis and psychological studies show that the facial resemblance differs from member to member and depends on image segments. First, we estimate the degree of resemblance of each member’s image segment. Then, we propose a novel method to fuse similarity of each member’s facial image segments to perform family verification. Employing the proposed approach on the collected 5,400-sample family database achieves considerable improvement compared to the state-of-the-art fusion rule in three designated test scenarios. Experimental results also show that the proposed approach could estimate the similarity slightly more accurate than human perception. We believe the public availableness of the database may advance the development in this domain.

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Abbreviations

\(F_{p}\) :

p-th family

\(M_{p}\) :

Members of p-th family

\(n_{pq}\) :

Number of total images for q-th member from p-th family

\(X_{pq}\) :

Face sample of q-th member from p-th family

\(L\) :

Number of regions faces are divided into

\(\hbox {S}=\left[ \hbox {S}_1,\ldots , \hbox {S}_{\mathrm{L}}\right] \) :

Vector of all patches’ match scores

\(f_{g,p}(S)\) :

The conditional joint density of \(L\) match scores for p-th family

\(f_{i}(S)\) :

The conditional joint density of \(L\) match scores for imposter samples

\(H_{0}\) :

The hypothesis of belonging to non-family members

\(H_{1}\) :

The hypothesis of belonging to p-th family

\(\eta \) :

Threshold

\({U}_{{q}}\) :

The set of all samples belong to q-th member

\(\hat{f}_{p,r,q} (Y \epsilon F_p)\) :

The estimated marginal density function of the genuine q-th member’s r-th patch samples of p-th family

References

  1. Alvergne, A., Oda, R., Faurie, C., Matsumoto-Oda, A., Durand, V., Raymond, M.: Cross-cultural perceptions of facial resemblance between kin. J. Vis. 9(6), 23.1–10 (2009). doi:10.1167/9.6.23/9/6/23

    Google Scholar 

  2. DeBruine, L., Jones, B., Little, A., Perrett, D.: Social perception of facial resemblance in humans. Arch. Sex Behav. 37(1), 64–77 (2008). doi:10.1007/s10508-007-9266-0

    Article  Google Scholar 

  3. Platek, S.M., Keenan, J.P., Mohamed, F.B.: Sex differences in the neural correlates of child facial resemblance: an event-related fMRI study. Neuroimage 25(4), 1336–1344 (2005). doi:10.1016/j.neuroimage.2004.12.037

    Article  Google Scholar 

  4. Platek, S.M., Kemp, S.M.: Is family special to the brain? An event-related fMRI study of familiar, familial, and self-face recognition. Neuropsychologia 47(3), 849–858 (2009). doi:10.1016/j.neuropsychologia.2008.12.027

    Article  Google Scholar 

  5. Ghahramani, M., Wang, H.L., Yau, W.Y., Teoh, E.K.: Unseen family member classification using mixture of experts. In: 2010 the 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), 15–17 June 2010, pp. 336–339. (2010)

  6. Ghahramani, M., Yau, W.Y., Teoh, E.K.: Family facial patch resemblance extraction. Paper presented at the Proceedings of the 10th Asian Conference on Computer vision—volume Part II. Queenstown, New Zealand (2010)

  7. Ruogu, F., Tang, K.D., Snavely, N., Tsuhan, C.: Towards computational models of kinship verification. In: 2010 17th IEEE International Conference on Image Processing (ICIP), 26–29 Sept 2010, pp. 1577–1580. (2010)

  8. Is DNA on the way? Biom. Technol. Today 2009(10), 2–3 (2009)

  9. Sedlak, A.J., Finkelhor, D., Hammer, H., Schultz., D.J.: National Estimates of Missing Children: An Overview, p. 5. Office of Juvenile Justice and Delinquency Prevention, Office of Justice Programs, Washington, D.C. (2002)

  10. Alvergne, A., Faurie, C., Raymond, M.: Differential facial resemblance of young children to their parents: who do children look like more? Evol. Hum. Behav. 28(2), 135–144 (2007). doi:10.1016/j.evolhumbehav.2006.08.008

    Article  Google Scholar 

  11. Dal Martello, M.F., Maloney, L.T.: Where are kin recognition signals in the human face? J. Vis. 6(12) (2006). doi:10.1167/6.12.2

  12. DeBruine, L.M., Smith, F.G., Jones, B.C., Roberts, S.C., Petrie, M., Spector, T.D.: Kin recognition signals in adult faces. Vis. Res. 49(1), 38–43 (2009). doi:10.1016/j.visres.2008.09.025

    Article  Google Scholar 

  13. Burch, R.L., Gallup, G.G.: Perceptions of paternal resemblance predict family violence. Evol. Hum. Behav. 21(6), 429–435 (2000). doi:S1090513800000568

    Article  Google Scholar 

  14. Meissner, C.A., Brigham, J.C.: Thirty Years of Investigating the Own-Race Bias in Memory for Faces: A Meta-Analytic Review. Psychology, Public Policy, and Law 7(1), 3–35 (2001)

    Article  Google Scholar 

  15. Das, M., Loui, A.C.: Automatic face-based image grouping for albuming. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 3724, pp. 3726–3731 (2003)

  16. Kanade, T.: Picture Processing System by Computer Complex and Recognition of Human Faces. Doctoral Dissertation, Kyoto University

  17. Xia, S., Shao, M., Fu, Y.: Kinship verification through transfer learning. Paper Presented at the Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 3. Barcelona, Catalonia, Spain (2011)

  18. Ghahramani, M., Dang, N.M., Teoh, E.K., Yau, W.Y.: A novel approach to remove redundant Gabor wavelets for family classification. In: 11th IEEE International Conference on Control Automation Robotics and Vision (ICARCV), 7–10 Dec 2010, pp. 2537–2542 (2010)

  19. Marquardt, S.R.: Marquardt Beauty Analysis. http://www.beautyanalysis.com

  20. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2001, CVPR 2001, vol. 511, pp. I-511–I-518. (2001)

  21. Liu, C.: Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 572–581 (2004)

    Article  Google Scholar 

  22. Ahonen, T., Hadid, A., Pietikainen, M.: Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Trans. Pattern Anal. Mach. Intell 28(12), 2037–2041 (2006). http://dx.doi.org/10.1109/TPAMI.2006.244

  23. Li, S., Jain, A.: Handbook of Face Recognition. Springer, Berlin (2005)

  24. Jian-Gang, W., Jun, L., Wei-Yun, Y., Sung, E.: Boosting dense SIFT descriptors and shape contexts of face images for gender recognition. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 13–18 June 2010, pp. 96–102. (2010)

  25. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Paper Presented at the Proceedings of the Second European Conference on Computational Learning Theory

  26. Zhou, M., Wei, H.: Face Verification Using GaborWavelets and AdaBoost. Paper Presented at the Proceedings of the 18th International Conference on Pattern Recognition, vol. 01

  27. Vezhnevets, A.: GML AdaBoost Matlab Toolbox. Moscow

  28. Nandakumar, K., Yi, C., Dass, S.C., Jain, A.K.: Likelihood ratio-based biometric score fusion. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 342–347 (2008)

    Article  Google Scholar 

  29. Ross, A.A., Nandakumar, K., Jain, A.K.: Levels of fusion in biometrics. In: Handbook of Multibiometrics. Springer, Berlin (2006)

  30. Toh, K.A., Xudong, J., Wei-Yun, Y.: Exploiting global and local decisions for multimodal biometrics verification. IEEE Trans. Signal Process. 52(10), 3059–3072 (2004)

    Article  Google Scholar 

  31. Ulery, B., Hicklin, A.R., Watson, C., Fellner, W., Hallinan, P.: Studies of Biometric Fusion, In: NIST, vol. IR 7346. (2006)

  32. Prabhakar, S., Jain, A.K.: Decision-Level Fusion in Fingerprint Verification, vol. 35, issue no. 4. Elsevier, Kidlington, ROYAUME-UNI (2002)

  33. Nandakumar, K., Ross, A., Jain, A.K.: Biometric fusion: does modeling correlation really matter? Paper Presented at the Proceedings of the Thirrd IEEE International Conference on Biometrics: Theory, Applications and systems. Washington, D.C., USA (2009)

  34. Gallagher, A.C., Tsuhan, C.: Clothing cosegmentation for recognizing people. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008, 23–28 June 2008, pp. 1–8. (2008)

  35. Milborrow, S., Nicolls, F.: Locating Facial Features with an Extended Active Shape Model. Paper Presented at the Proceedings of the 10th European Conference on Computer Vision: Part IV, Marseille, France (2008)

  36. Figueiredo, M.A.T., Jain, A.K.: Unsupervised learning of finite mixture models. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 381–396 (2002). doi:10.1109/34.990138

    Article  Google Scholar 

  37. http://www.prtools.org/

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Acknowledgments

We would like to thank Dr. Karthik Nandakumar for his very helpful comments.

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Authors and Affiliations

  1. School of Electrical and Electronics Engineering, 50 Nanyang Drive, Nanyang Technological University, Singapore, 639798, Singapore

    Mohammad Ghahramani & Eam Khwang Teoh

  2. Institute for Infocomm Research (I²R), 1 Fusionopolis way, Singapore, 138632, Singapore

    Wei-Yun Yau

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  1. Mohammad Ghahramani
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Correspondence to Mohammad Ghahramani.

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Ghahramani, M., Yau, WY. & Teoh, E.K. Family verification based on similarity of individual family member’s facial segments. Machine Vision and Applications 25, 919–930 (2014). https://doi.org/10.1007/s00138-013-0566-1

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