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Cubic norm and kernel-based bi-directional PCA: toward age-aware facial kinship verification

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Abstract

A recent challenge in computer vision is exploring the cardinality of a relationship among multiple visual entities to answer questions like whether the subjects in a photograph have a kin relationship. This paper tackles kinship recognition from the aging viewpoint in which the system could find the parent of a child where the input image of the parent belongs to the age range that is lower than the child is. Technical contributions of this research are twofold. (1) An efficient discriminative feature space is constructed by proposing kernelized bi-directional PCA to form a topological cubic feature space. Cubic feature space in conjunction with the introduced cubic norm is used to solve the kinship problem. (2) To fill the gap of aging effect in finding a kin relation, a semi-supervised learning paradigm is proposed. To do this, first, the pooling layer of a convolutional neural network is modified to do a soft pooling. Then, the last pooling layer, as a rich feature vector, is fed into density-based spatial clustering of applications with noise algorithm. This pre-classification phase would be useful when there is no aggregation on how many classes should be used in the age group estimation task. Finally, by adding kernel computation to sparse representation classifier, the age classification is done. Evaluation of the proposed method on five publicly available facial kinship datasets shows the superiority of the proposed method over both the state-of-the-art kinship verification methods and what is known as human decision-making.

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References

  1. Li, Y., Peng, Z., Liang, D., Chang, H., Cai, Z.: Facial age estimation by using stacked feature composition and selection. Vis. Comput. 32, 1525–1536 (2016)

    Article  Google Scholar 

  2. Hou, X., Ding, S., Ma, L.: Robust feature encoding for age-invariant face recognition. In: 2016 IEEE International Conference on Multimedia and Expo (ICME), IEEE, pp. 1–6 (2016)

  3. Shu, X., Tang, J., Lai, H., Niu, Z., Yan, S.: Kinship-guided age progression. Pattern Recognit. 59, 156–167 (2016)

    Article  Google Scholar 

  4. Vieira, T.F., Bottino, A., Laurentini, A., De Simone, M.: Detecting siblings in image pairs. Vis. Comput. 30, 1333–1345 (2014)

    Article  Google Scholar 

  5. Zuo, W., Wang, K., Zhang, D.: Bi-directional PCA with assembled matrix distance metric. In: IEEE International Conference on Image Processing, 2005. ICIP 2005. IEEE, pp. II-958 (2005)

  6. Lee, H., Pham, P., Largman, Y., Ng, A.Y.: Unsupervised feature learning for audio classification using convolutional deep belief networks. In: Advances in Neural Information Processing Systems, pp. 1096–1104 (2009)

  7. Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, vol. 96, issue 34, pp. 226–231 (1996)

  8. Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: which helps face recognition? In: 2011 IEEE international conference on Computer Vision (ICCV), IEEE, pp. 471–478 (2011)

  9. Kwon, Y.H.: Age classification from facial images. In: 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1994. Proceedings CVPR’94. IEEE, pp. 762–767 (1994)

  10. Ramanathan, N., Chellappa, R., Biswas, S.: Age progression in human faces: a survey. J. Vis. Lang. Comput. 15, 3349–3361 (2009)

    Google Scholar 

  11. Dehshibi, M.M., Bastanfard, A.: A new algorithm for age recognition from facial images. Signal Process. 90, 2431–2444 (2010)

    Article  MATH  Google Scholar 

  12. Geng, X., Zhou, Z.-H., Smith-Miles, K.: Automatic age estimation based on facial aging patterns. IEEE Trans. Pattern Anal. Mach. Intell. 29, 2234–2240 (2007)

    Article  Google Scholar 

  13. Luu, K., Ricanek, K., Bui, T.D., Suen, C.Y.: Age estimation using active appearance models and support vector machine regression. In: IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, 2009. BTAS’09. IEEE, pp. 1–5 (2009)

  14. Fu, Y., Huang, T.S.: Human age estimation with regression on discriminative aging manifold. IEEE Trans. Multimed. 10, 578–584 (2008)

    Article  Google Scholar 

  15. Yan, S., Liu, M., Huang, T.S.: Extracting age information from local spatially flexible patches. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE, pp. 737–740 (2008)

  16. Ni, B., Song, Z., Yan, S.: Web image mining towards universal age estimator. In: Proceedings of the 17th ACM International Conference on Multimedia, ACM, pp. 85–94 (2009)

  17. Suo, J., Wu, T., Zhu, S., Shan, S., Chen, X., Gao, W.: Design sparse features for age estimation using hierarchical face model. In: 8th IEEE International Conference on Automatic Face and Gesture Recognition, 2008. FG’08. IEEE, pp. 1–6 (2008)

  18. Gunay, A., Nabiyev, V.V.: Automatic age classification with LBP. In: 23rd International Symposium on Computer and Information Sciences, 2008. ISCIS’08. IEEE, pp. 1–4 (2008)

  19. Guo, G., Mu, G., Fu, Y., Huang, T.S.: Human age estimation using bio-inspired features. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE, pp. 112–119 (2009)

  20. Chao, W.-L., Liu, J.-Z., Ding, J.-J.: Facial age estimation based on label-sensitive learning and age-oriented regression. Pattern Recognit. 46, 628–641 (2013)

    Article  Google Scholar 

  21. Geng, X., Zhou, Z.-H., Zhang, Y., Li, G., Dai, H.: Learning from facial aging patterns for automatic age estimation. In: Proceedings of the 14th ACM International Conference on Multimedia, ACM, pp. 307–316 (2006)

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

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

    Article  Google Scholar 

  24. Zhou, X., Lu, J., Hu, J., Shang, Y.: Gabor-based gradient orientation pyramid for kinship verification under uncontrolled environments. In: Proceedings of the 20th ACM international Conference on Multimedia, ACM, pp. 725–728 (2012)

  25. Dehshibi, M.M., Shanbezadeh, J., Alavi, M.: Facial family similarity recognition using Local Gabor Binary Pattern Histogram Sequence. In: 2012 12th International Conference on Hybrid Intelligent Systems (HIS), IEEE, pp. 219–224 (2012)

  26. Kohli, N., Singh, R., Vatsa, M.: Self-similarity representation of Weber faces for kinship classification. In: 2012 IEEE Fifth International Conference on Biometrics: theory, applications and systems (BTAS), IEEE, pp. 245–250 (2012)

  27. Xia, S., Shao, M., Fu, Y.: Kinship verification through transfer learning. In: IJCAI, pp. 2539–2544 (2011)

  28. Lu, J., Zhou, X., Tan, Y.-P., Shang, Y., Zhou, J.: Neighborhood repulsed metric learning for kinship verification. IEEE Trans. Pattern Anal. Mach. Intell. 36, 331–345 (2014)

    Article  Google Scholar 

  29. Yan, H., Lu, J., Deng, W., Zhou, X.: Discriminative multimetric learning for kinship verification. IEEE Trans. Inf. Forensics Secur. 9, 1169–1178 (2014)

    Article  Google Scholar 

  30. Zhou, X., Shang, Y., Yan, H., Guo, G.: Ensemble similarity learning for kinship verification from facial images in the wild. Inf. Fusion 32, 40–48 (2016)

    Article  Google Scholar 

  31. Golub, G.H., Van Loan, C.F.: Matrix Computations. JHU Press, Baltimore (2012)

    MATH  Google Scholar 

  32. Yang, J., Zhang, D., Frangi, A.F., Yang, J.-Y.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26, 131–137 (2004)

    Article  Google Scholar 

  33. Xu, A., Jin, X., Jiang, Y., Guo, P.: Complete two-dimensional PCA for face recognition. In: 18th International Conference on Pattern Recognition, 2006. ICPR 2006. IEEE, pp. 481–484 (2006)

  34. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors, arXiv preprint arXiv:1207.0580 (2012)

  35. Han, H., Otto, C., Jain, A.K.: Age estimation from face images: human vs. machine performance. In: 2013 International Conference on Biometrics (ICB), IEEE, pp. 1–8 (2013)

  36. Du, H., Hu, Q., Jiang, M., Zhang, F.: Two-dimensional principal component analysis based on Schatten p-norm for image feature extraction. J. Vis. Commun. Image Represent. 32, 55–62 (2015)

    Article  Google Scholar 

  37. Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 11, 2837–2854 (2010)

    MathSciNet  MATH  Google Scholar 

  38. Bastanfard, A., Nik, M.A., Dehshibi, M.M.: Iranian face database with age, pose and expression. In: International Conference on Machine Vision, 2007. ICMV 2007. IEEE, pp. 50–55 (2007)

  39. Cootes, T.: FG-NET aging database. In: Face and Gesture Recognition Working Group. University of Manchester, UK (2002). www-prima.inrialpes.fr/FGnet/

  40. Ricanek, K., Tesafaye, T.: Morph: a longitudinal image database of normal adult age-progression. In: 7th International Conference on Automatic Face and Gesture Recognition (FGR06), IEEE, pp. 341–345 (2006)

  41. Kyaw, S.P., Wang, J.-G., Teoh, E.K.: Web image mining for facial age estimation. In: 2013 9th International Conference on Information, Communications and Signal Processing (ICICS), IEEE, pp. 1–5 (2013)

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

    Article  Google Scholar 

  43. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28, 2037–2041 (2006)

    Article  MATH  Google Scholar 

  44. Bocklet, T., Maier, A., Bauer, J.G., Burkhardt, F., Noth, E.: Age and gender recognition for telephone applications based on gmm supervectors and support vector machines. In: 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE, pp. 1605–1608 (2008)

  45. Meyers, E., Wolf, L.: Using biologically inspired features for face processing. Int. J. Comput. Vis. 76, 93–104 (2008)

    Article  Google Scholar 

  46. Steinley, D.: Properties of the Hubert–Arable Adjusted Rand Index. Psychol. Methods 9, 386 (2004)

    Article  Google Scholar 

  47. Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)

    MATH  Google Scholar 

  48. Guo, G., Fu, Y., Dyer, C.R., Huang, T.S.: Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Trans. Image Process. 17, 1178–1188 (2008)

    Article  MathSciNet  Google Scholar 

  49. Chang, K.-Y., Chen, C.-S., Hung, Y.-P.: Ordinal hyperplanes ranker with cost sensitivities for age estimation. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 585–592 (2011)

  50. Yan, S., Wang, H., Tang, X., Huang, T.S.: Learning auto-structured regressor from uncertain nonnegative labels. In: 2007 IEEE 11th International Conference on Computer Vision, IEEE, pp. 1–8 (2007)

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

  52. Xia, S., Shao, M., Luo, J., Fu, Y.: Understanding kin relationships in a photo. IEEE Trans. Multimed. 14, 1046–1056 (2012)

    Article  Google Scholar 

  53. Xia, S., Shao, M., Fu, Y.: Kinship verification through transfer learning. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, pp. 2539 (2011)

  54. Fang, R., Gallagher, A.C., Chen, T., Loui, A.: Kinship classification by modeling facial feature heredity. In: 2013 IEEE International Conference on Image Processing, IEEE, pp. 2983–2987 (2013)

  55. Qin, X., Tan, X., Chen, S.: Tri-subject kinship verification: understanding the core of a family. IEEE Trans. Multimed. 17, 1855–1867 (2015)

    Article  Google Scholar 

  56. Dehghan, A., Ortiz, E.G., Villegas, R., Shah, M.: Who do i look like? Determining parent-offspring resemblance via gated autoencoders. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1757–1764 (2014)

  57. Hu, J., Lu, J., Tan, Y.-P.: Discriminative deep metric learning for face verification in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1875–1882 (2014)

  58. Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: Proceedings of the 24th International Conference on Machine Learning, ACM, pp. 209–216 (2007)

  59. Weinberger, K.Q., Blitzer, J., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. In: Advances in Neural Information Processing Systems, pp. 1473–1480 (2005)

  60. Plomin, R., Daniels, D.: Why are children in the same family so different from one another? Behav. Brain Sci. 10, 1–16 (1987)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mohammad Mahdi Dehshibi

  2. Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

    Jamshid Shanbehzadeh

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  1. Mohammad Mahdi Dehshibi
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  2. Jamshid Shanbehzadeh
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Correspondence to Jamshid Shanbehzadeh.

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Dehshibi, M.M., Shanbehzadeh, J. Cubic norm and kernel-based bi-directional PCA: toward age-aware facial kinship verification. Vis Comput 35, 23–40 (2019). https://doi.org/10.1007/s00371-017-1442-1

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