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Detecting siblings in image pairs

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Abstract

In everyday life, face similarity is an important kinship clue. Computer algorithms able to infer kinship from pairs of face images could be applied in forensics, image retrieval and annotation, and historical studies. So far, little work in this area has been presented, and only one study, using a small set of low quality images, tackles the problem of identifying siblings pairs. The purpose of our paper is to present a comprehensive investigation on this subject, aimed at understanding which are, on the average, the most relevant facial features, how effective can be computer algorithms for detecting siblings pairs, and if they can outperform human evaluation. To avoid problems due to low quality pictures and uncontrolled imaging conditions, as for the heterogeneous datasets collected for previous researches, we prepared a database of high quality pictures of sibling pairs, shot in controlled conditions and including frontal, profile, expressionless, and smiling faces. Then we constructed various classifiers of image pairs using different types of facial data, based on various geometric, textural, and holistic features. The classifiers were first tested separately, and then the most significant facial data, selected with a two stage feature selection algorithm were combined into a unique classifier. The discriminating ability of the automatic classifier combining features of different nature has been found to outperform that of a panel of human raters. We also show the good generalization capabilities of the algorithm by applying the classifier, in a cross-database experiment, to a low quality database of images collected from the Internet.

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References

  1. Abdel-Hakim, A., Farag, A.: Csift: a sift descriptor with color invariant characteristics. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, vol. 2, pp. 1978–1983 (2006)

    Google Scholar 

  2. Bailenson, J.N., Iyengar, S., Yee, N., Collins, N.A.: Facial similarity between voters and candidates causes influence. Public Opin. Q. 72(5), 935–961 (2008)

    Article  Google Scholar 

  3. Berretti, S., Ben Amor, B., Daoudi, M., Bimbo, A.: 3d facial expression recognition using sift descriptors of automatically detected keypoints. Vis. Comput. 27(11), 1021–1036 (2011)

    Article  Google Scholar 

  4. Bianconi, F., Fernández, A., Mancini, A.: Assessment of rotation-invariant texture classification through {G}abor filters and discrete {F}ourier transform. In: Proceedings of the 20th International Congress on Graphical Engineering (XX INGEGRAF), Valencia, Spain (2008)

    Google Scholar 

  5. Bottino, A., Cumani, S.: A fast and robust method for the identification of face landmarks in profile images. WSEAS Trans. Comput. 7(8), 1250–1259 (2008)

    Google Scholar 

  6. Bottino, A., Laurentini, A.: The analysis of facial beauty: an emerging area of research in pattern analysis. In: Lecture Notes in Computer Science, vol. 6111/2010, pp. 425–435. Springer, Berlin (2010)

    Google Scholar 

  7. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011)

    Article  Google Scholar 

  8. Chang, C.C., Lin, C.J.: LIBSVM—a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm/

  9. Dal Martello, M.F., Maloney, L.T.: Where are kin recognition signals in the human face? J. Vis. 6(12), 1356–1366 (2006)

    Article  Google Scholar 

  10. 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)

    Article  Google Scholar 

  11. Fang, R., Tang, K., Snavely, N., Chen, T.: Towards computational models of kinship verification. In: 17th IEEE International Conference on Image Processing (ICIP), 2010, pp. 1577–1580 (2010)

    Chapter  Google Scholar 

  12. Fu, Y., Guo, G., Huang, T.: Age synthesis and estimation via faces: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 1955–1976 (2010)

    Article  Google Scholar 

  13. GmbH, C.: FaceVACS-SDK Version 8.3 C++ Reference Manual. Cognitec GmbH

  14. Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? Metric learning approaches for face identification. In: IEEE 12th International Conference on Computer Vision, 2009, pp. 498–505 (2009)

    Chapter  Google Scholar 

  15. Guo, G., Wang, X.: Kinship measurement on salient facial features. IEEE Trans. Instrum. Meas. 61, 2322–2325 (2012)

    Article  Google Scholar 

  16. Hamilton, W.D.: The genetical evolution of social behaviour. I and ii. J. Theor. Biol. 7(1), 1–16 (1964)

    Article  Google Scholar 

  17. Kaminski, G., Dridi, S., Graff, C., Gentaz, E.: Human ability to detect kinship in strangers’ faces: effects of the degree of relatedness. Proc. - Royal Soc., Biol. Sci. 276(1670), 3193–3200 (2009)

    Article  Google Scholar 

  18. Laurentini, A., Bottino, A., DeSimone, M., Vieira, T.: A new problem in face image analysis: finding kinship clues for siblings pairs. In: Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, vol. 119, pp. 405–410 (2012)

    Google Scholar 

  19. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  20. Milborrow, S.: Active shape models with Stasm. http://www.milbo.users.sonic.net/stasm/

  21. Milborrow, S., Nicolls, F.: Locating facial features with an extended active shape model. In: Proceedings of the 10th European Conference on Computer Vision: Part IV, ECCV 2008, pp. 504–513. Springer, Berlin, Heidelberg (2008)

    Google Scholar 

  22. Ng, A.Y.: Feature selection, l1 vs. l2 regularization, and rotational invariance. In: Proceedings of the Twenty-First International Conference on Machine Learning, ICML ’04, p. 78. ACM, New York (2004)

    Chapter  Google Scholar 

  23. Nosaka, R., Suryanto, C.H., Fukui, K.: Rotation invariant co-occurrence among adjacent lbps. In: Proceedings of the ACCV2012 Workshop LBP2012, pp. 1–11 (2012)

    Google Scholar 

  24. Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, 1994. Vol. 1—Conference A: Computer Vision Amp; Image Processing, vol. 1, pp. 582–585 (1994)

    Google Scholar 

  25. Pantic, M., Rothkrantz, L.: Toward an affect-sensitive multimodal human-computer interaction. Proc. IEEE 91(9), 1370–1390 (2003)

    Article  Google Scholar 

  26. Park, J., Schaller, M., Van Vugt, M.: The psychology of human kin recognition: heuristic cues, erroneous inferences, and their implications. Rev. Gen. Psychol. 12(3), 215–235 (2008)

    Article  Google Scholar 

  27. Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)

    Article  Google Scholar 

  28. Phillips, P., Scruggs, W., O’Toole, A., Flynn, P., Bowyer, K., Schott, C., Sharpe, M.: Frvt 2006 and ice 2006 large-scale experimental results. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 831–846 (2010)

    Article  Google Scholar 

  29. Shao, M., Xia, S., Fu, Y.: Genealogical face recognition based on UB KinFace database. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2011, pp. 60–65 (2011)

    Google Scholar 

  30. Sirovich, L.M.K.: Low-dimensional procedure for the characterization of human faces. J. Opt. Soc. Am. A 4(3), 519–524 (1987)

    Article  Google Scholar 

  31. Somanath, G., Kambhamettu, C.: Can faces verify blood-relations? In: IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) (2012). https://www.eecis.udel.edu/wiki/vims/index.php/Main/VADANA

    Google Scholar 

  32. Somanath, G., Rohith, M., Kambhamettu, C.: Vadana: a dense dataset for facial image analysis. In: IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011, pp. 2175–2182 (2011)

    Chapter  Google Scholar 

  33. van de Sande, K., Gevers, T., Snoek, C.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)

    Article  Google Scholar 

  34. Whitney, A.: A direct method of nonparametric measurement selection. IEEE Trans. Comput. C-20(9), 1100–1103 (1971)

    Article  MathSciNet  Google Scholar 

  35. Wolf, L., Bileschi, S.: Combining variable selection with dimensionality reduction. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 801–806 (2005)

    Google Scholar 

  36. Xia, S., Shao, M., Fu, Y.: Kinship verification through transfer learning. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Volume Three, IJCAI’11, pp. 2539–2544. AAAI Press, Menlo Park (2011)

    Google Scholar 

  37. Zhang, L., Razdan, A., Farin, G., Femiani, J., Bae, M., Lockwood, C.: 3d face authentication and recognition based on bilateral symmetry analysis. Vis. Comput. 22(1), 43–55 (2006)

    Article  MATH  Google Scholar 

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Acknowledgements

The authors thank Dr. Gowri Somanath at the University of Delaware for making available the VADANA database and for providing us the datasets used in our experiments. A preliminary version of this work was presented in [18], where we analyzed the use of holistic techniques only.

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Authors and Affiliations

  1. DAUIN, Politecnico di Torino, C.so Duca Degli Abruzzi, 24, 10129, Turin, Italy

    Tiago F. Vieira, Andrea Bottino, Aldo Laurentini & Matteo De Simone

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  1. Tiago F. Vieira
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  2. Andrea Bottino
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  3. Aldo Laurentini
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  4. Matteo De Simone
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Correspondence to Andrea Bottino.

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Vieira, T.F., Bottino, A., Laurentini, A. et al. Detecting siblings in image pairs. Vis Comput 30, 1333–1345 (2014). https://doi.org/10.1007/s00371-013-0884-3

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