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Multi-feature-Based Facial Age Estimation Using an Incomplete Facial Aging Database

  • Research Article - Computer Engineering and Computer Science
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Abstract

Age estimation from face images is a complex process as it varies from person to person, affected by various intrinsic factors (such as genetic and hormonal) and extrinsic factors (such as environmental, lifestyle, illumination, pose and expression). In this paper, an age estimation system has been proposed that preserves personalized aging trait as well as being robust to change in appearance, shape, wrinkle, texture, expression, pose and illumination of a face. The Golden ratio-based face cropping maintains uniformity of facial regions among faces irrespective of age, gender and race. Local, global and combinational features are extracted to handle the variations of intrinsic and extrinsic factors of aging. The experiment is conducted on FG-NET-AD and MORPH Album-2 facial aging databases. As the existing facial aging databases are incomplete, dealing with strong person-specificity, and high within-range variance; so the dominance of one age-group on other is resolved by feature filling that is carried out by a multi-feature-based modified expectation maximization algorithm. The age estimation is carried out by a three-level hierarchical classifier based on SVM and SVR by choosing suitable combination of hybrid feature sets. The experimental results show the superiority of proposed approach as compared to some of the existing age estimation approaches available in the literature.

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References

  1. Lanitis, A.; Draganova, C.; Christodoulou, C.: Comparing different classifiers for automatic age estimation. IEEE Trans. Syst. Man Cybern. B Cybern. 34(1), 621–628 (2004)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Farage, M.A.; Miller, K.W.; Elsner, P.; Maibach, H.I.: Intrinsic and extrinsic factors in skin ageing: a review. Int. J. Cosmet. Sci. 30(2), 1468–2494 (2008)

    Article  Google Scholar 

  4. Liu, L.; Xiong, C.; Zhang, H.; Niu, Z.; Wang, M.; Yan, S.: Deep aging face verification with large gaps. IEEE Trans. Multimed. 18(1), 64–75 (2016)

    Article  Google Scholar 

  5. Bianco, S.: Large age-gap face verification by feature injection in deep networks. Pattern Recogn. Lett. 90(Supplement C), 36–42 (2017)

    Article  Google Scholar 

  6. Li, S.Z.; Jain, A.K. (eds.): Handbook of Face Recognition, 2nd edn. Springer-Verlag, London (2011)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  8. Alley, T.R. (ed.): Social and Applied Aspects of Perceiving Faces. Lawrence Erlbaum Associates, Hillsdale (1988)

    Google Scholar 

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

  10. Ramanathan, N.; Chellappa, R.: Face verification across age progression. IEEE Trans. Image Process. 15(11), 3349–3361 (2006)

    Article  Google Scholar 

  11. Choi, S.E.; Lee, Y.J.; Lee, S.J.; Park, K.R.; Kim, J.: Age estimation using a hierarchical classifier based on global and local facial features. Pattern Recogn. 44(6), 1262–1281 (2011)

    Article  Google Scholar 

  12. Panis, G.; Lanitis, A.; Tsapatsoulis, N.; Cootes, T.F.: Overview of research on facial ageing using the FG-NET ageing database. IET Biomet. 5(2), 37–46 (2016)

    Article  Google Scholar 

  13. 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), pp. 341–345, (2006)

  14. Schwind, V.: The Golden Ratio in 3d Human Face Modeling. Stuttgart Media University, Stuttgart (2015)

    Google Scholar 

  15. Sahoo, T.K.; Banka, H.: New hybrid PCA-based facial age estimation using inter-age group variation-based hierarchical classifier. Arab. J. Sci. Eng. 42(8), 3337–3355 (2017)

    Article  Google Scholar 

  16. 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 Annual ACM International Conference on Multimedia, MULTIMEDIA ’06, pp. 307–316, (2006)

  17. Kwon, Y.H.; da Vitoria Lobo, N.: Age classification from facial images. Comput. Vis. Image Underst. 74(1), 1–21 (1999)

    Article  Google Scholar 

  18. Horng, W.-B.; Lee, C.-P.; Chen, C.-W.: Classification of age groups based on facial features. Tamkang J. Sci. Eng. 4(3), 183–192 (2001)

    Google Scholar 

  19. Matthews, I.; Baker, S.: Active appearance models revisited. Int. J. Comput. Vision 60(2), 135–164 (2004)

    Article  Google Scholar 

  20. Chao, W.-L.; Liu, J.-Z.; Ding, J.-J.: Facial age estimation based on label-sensitive learning and age-oriented regression. Pattern Recogn. 46(3), 628–641 (2013)

    Article  Google Scholar 

  21. Chen, Y.-L.; Hsu, C.-T.: Subspace learning for facial age estimation via pairwise age ranking. IEEE Trans. Inf. Forensics Secur. 8(12), 2164–2176 (2013)

    Article  Google Scholar 

  22. Huerta, I.; Fernández, C.; Prati, A.: Facial age estimation through the fusion of texture and local appearance descriptors. In: ECCV Workshops (2), pp. 667–681 (2014)

    Google Scholar 

  23. Wang, S.; Tao, D.; Yang, J.: Relative attribute SVM+ learning for age estimation. IEEE Trans. Cybern. 46(3), 827–839 (2016)

    Article  Google Scholar 

  24. Pontes, J.K.; Britto, A.S.; Fookes, C.; Koerich, A.L.: A flexible hierarchical approach for facial age estimation based on multiple features. Pattern Recogn. 54(Supplement C), 34–51 (2016)

    Article  Google Scholar 

  25. Ng, C.C.; Yap, M.H.; Cheng, Y.T.; Hsu, G.S.: Hybrid ageing patterns for face age estimation. Image Vis. Comput. (2017)

  26. Lal, S.; Chandra, M.: Efficient algorithm for contrast enhancement of natural images. Int. Arab J. Inf. Technol. 11(1), 95–102 (2014)

    Google Scholar 

  27. Chen, S.; Zhu, Y.: Subpattern-based principal component analysis. Pattern Recogn. 37, 1081–1083 (2004)

    Article  Google Scholar 

  28. Cootes, T.F.; Taylor, C.J.; Cooper, D.H.; Graham, J.: Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)

    Article  Google Scholar 

  29. Manjunath, B.S.; Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)

    Article  Google Scholar 

  30. Ahonen, T.; Hadid, A.; Pietikinen, M.: Face recognition with local binary patterns. In: 8th European Conference on Computer Vision, Proceedings, Part I, pp. 469–481 (2004)

    Google Scholar 

  31. Manesh, F.S.; Ghahramani, M.; Tan, Y.P.: Facial part displacement effect on template-based gender and ethnicity classification. In: 2010 11th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 1644–1649. IEEE (2010)

  32. Mostafa, L.; Abdelazeem, S.: Face detection based on skin color using neural networks. In: GVIP 05 Conference, vol. 7, pp. 19–21 (2005)

  33. Hjelmås, E.; Low, B.K.: Face detection: a survey. Comput. Vis. Image Underst. 83(3), 236–274 (2001)

    Article  Google Scholar 

  34. Chen, R.C.; Yang, K.L.; Chang, C.K.: The detection of facial features based on floating parameters and biometric. In: International Conference on Business and Information(BAI2013), pp. 178–187 (2013)

  35. Liu, J.; Ma, Y.; Duan, L.; Wang, F.; Liu, Y.: Hybrid constraint SVR for facial age estimation. Sig. Process. 94, 576–582 (2014)

    Article  Google Scholar 

  36. Guo, G.; Yun, F.; Dyer, C.R.; Huang, T.S.: Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Trans. Image Process. 17(7), 1178–1188 (2008)

    Article  MathSciNet  Google Scholar 

  37. Chang, K.Y; Chen, C.S.; Hung, Y.P.: A ranking approach for human ages estimation based on face images. In: 20th International Conference on Pattern Recognition (ICPR), pp. 3396–3399 (2010)

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

  39. Geng, X.; Yin, C.; Zhou, Z.-H.: Facial age estimation by learning from label distributions. IEEE Trans. Pattern Anal. Mach. Intell. 35(10), 2401–2412 (2013)

    Article  Google Scholar 

  40. Kumar, K.V.; Negi, A.: SubXPCA and a generalized feature partitioning approach to principal component analysis. Pattern Recogn. 41(4), 1398–1409 (2008)

    Article  Google Scholar 

  41. 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(CVPR), pp. 112–119 (2009)

  42. Jain, A.K.; Bhattacharjee, S.K.: Address block location on envelopes using gabor filters: supervised method. In: 11th IAPR International Conference on Pattern Recognition, Vol. II. Conference B: Pattern Recognition Methodology and Systems, Proceedings, pp. 264–267 (1992)

  43. Jain, A.K.; Ratha, N.K.; Lakshmanan, S.: Object detection using gabor filters. Pattern Recogn. 30, 295–309 (1997)

    Article  Google Scholar 

  44. Ojala, T.; Pietikinen, M.; Menp, T.: Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  45. Johnson, R.A.; Wichern, D.W. (eds.): Applied Multivariate Statistical Analysis. Prentice-Hall Inc, Upper Saddle River (1988)

    MATH  Google Scholar 

  46. Raudys, S.J.; Jain, A.K.: Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans. Pattern Anal. Mach. Intell. 13(3), 252–264 (1991)

    Article  Google Scholar 

  47. Liang, Y.; Wang, X.; Zhang, L.; Wang, Z.: A hierarchical framework for facial age estimation. Mathematical Problems in Engineering, 2014 (2014)

    Google Scholar 

  48. Turk, M.; Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Article  Google Scholar 

  49. Pietikainen, M.; Hadid, A.; Zhao, Y.; Ahonen, T.: Computer Vision Using Local Binary Patterns, Computational Imaging and Vision. Springer Verlag, London (2011)

    Book  Google Scholar 

  50. Guo, G.; Fu, Y.; Dyer, C.R.; Huang, T.S.: A probabilistic fusion approach to human age prediction. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1–6 (2008)

  51. Yan, S.; Wang, H.; Yun, F.; Yan, J.; Tang, X.; Huang, T.S.: Synchronized submanifold embedding for person-independent pose estimation and beyond. IEEE Trans. Image Process. 18(1), 202–210 (2009)

    Article  MathSciNet  Google Scholar 

  52. Suo, J.; Zhu, S.-C.; Shan, S.; Chen, X.: A compositional and dynamic model for face aging. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 385–401 (2010)

    Article  Google Scholar 

  53. Tao, W.; Turaga, P.; Chellappa, R.: Age estimation and face verification across aging using landmarks. IEEE Trans. Inf. Forensics Secur. 7(6), 1780–1788 (2012)

    Article  Google Scholar 

  54. Weng, R.; Lu, J.; Yang, G.; Tan, Y.P.: Multi-feature ordinal ranking for facial age estimation. In: 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–6 (2013)

  55. Gao, P.X.: Facial age estimation using clustered multi-task support vector regression machine. In: 21st International Conference on Pattern Recognition (ICPR), pp. 541–544 (2012)

  56. Wang, X.; Guo, R.; Kambhamettu, C.: Deeply-learned feature for age estimation. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 534–541 (2015)

  57. Duong, C.N.; Luu, K.; Quach, K.G.; Bui, T.D.: Beyond principal components: deep Boltzmann machines for face modeling. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4786–4794 (2015)

  58. Yi, D.; Lei, Z.; Li, S.Z.: Age estimation by multi-scale convolutional network. In: Asian Conference on Computer Vision, pp. 144–158. Springer (2014)

  59. Niu, Z.; Zhou, M.; Wang, L.; Gao, X.; Hua, G.: Ordinal regression with multiple output CNN for age estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4920–4928 (2016)

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India

    Tapan Kumar Sahoo & Haider Banka

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  1. Tapan Kumar Sahoo
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  2. Haider Banka
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Correspondence to Tapan Kumar Sahoo.

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Sahoo, T.K., Banka, H. Multi-feature-Based Facial Age Estimation Using an Incomplete Facial Aging Database. Arab J Sci Eng 43, 8057–8078 (2018). https://doi.org/10.1007/s13369-018-3293-0

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