IJMECS Vol. 7, No. 12, 8 Dec. 2015

Cover page and Table of Contents: PDF (size: 386KB)

Full Text (PDF, 386KB), PP.17-28

Views: 0 Downloads: 0

Facial age estimation, image processing, machine learning, pattern recognition, survey

Age is a human attribute which grows alongside an individual. Estimating human age is quite difficult for machine as well as humans, however there has been and are still ongoing efforts towards machine estimation of human age to a high level of accuracy. In a bid to improve the accuracy of age estimation from facial image, several approaches have been proposed many of which used Machine Learning algorithms. The several Machine Learning algorithms employed in these works have made significant impact on the results and of performances of the proposed age estimation approaches. In this paper, we examined and compared the performance of a number of Machine Learning algorithms used for age estimation in several previous works. Considering two publicly available facial ageing datasets (FG-NET and MORPH) which have been mostly used in previous works, we observed that Support Vector Machine (SVM) has been most popularly used and a combination/hybridization of SVM for classification (SVC) and regression (SVR) have shown the best performance so far. We also observed that the face modelling or feature extraction techniques employed significantly impacted the performance of age estimation algorithms.

Olufade F.W. Onifade, Damilola J. Akinyemi, "A Review on the Suitability of Machine Learning Approaches to Facial Age Estimation", International Journal of Modern Education and Computer Science (IJMECS), vol.7, no.12, pp.17-28, 2015. DOI:10.5815/ijmecs.2015.12.03

[1]Y. Fu, G. Guo, and T. S. Huang, “Age Synthesis and Estimation via Faces: A Survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 11, pp. 1955–1976, 2010.

[2]G. Guo and T. S. Huang, “Human Age Estimation Using Bio-inspired Features,” in IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 112–119.

[3]O. F. W. Onifade and J. D. Akinyemi, “A GW Ranking Approach for Facial Age Estimation,” Egypt. Comput. Sci. J., vol. 38, no. 3, pp. 63–74, 2014.

[4]D. Michie, D. J. Spiegelhalter, and C. C. Taylor, Machine Learning , Neural and Statistical Classification. 1994.

[5]X. Geng, Y. Fu, and K. Smith-Miles, “Automatic Facial Age Estimation,” in 11th Pacific Rim International Conference on Artificial Intelligence, 2010, pp. 1–130.

[6]K. Luu, K. Ricanek, T. D. Bui, and C. Y. Suen, “Age Estimation using Active Appearance Models and Support Vector Machine Regression,” in IEEE International Conference on Biometrics: Theory, Applications and System, 2009, pp. 314–318.

[7]G. Guo, Y. Fu, C. R. Dyer, and T. S. Huang, “Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression,” IEEE Trans. Image Process., vol. 17, no. 7, pp. 1178–1188, 2008.

[8]“FG-NET,” 2013. [Online]. Available: http://www.sting.cycollege.ac.cy/~alanitis/fgnetaging/index.htm. [Accessed: 17-Jun-2013].

[9]K. Ricanek and T. Tesafaye, “MORPH: A Longitudinal Image Database of Normal Adult Age-Progression,” in In IEEE 7th International Conference on Automatic Face and Gesture Recognition, 2006, pp. 341–345.

[10]“MORPH Non-Commercial Release Whitepaper,” 2007.

[11]O. F. W. Onifade and J. D. Akinyemi, “A Model of Correlated Ageing Pattern for Age Ranking,” Comput. Sci. Inf. Technol. (CSI T), vol. 4, no. 2, pp. 477–485, 2014.

[12]J. D. Akinyemi, “GWAgeER; A GroupWise Age-Ranking Approach to Age Estimation from Still Facial Image,” University of Ibadan, Ibadan, 2014.

[13]Y. H. Kwon and V. Lobo, “Age Classification from Facial Images,” Comput. Vis. Image Underst., vol. 74, no. 1, pp. 1–21, 1999.

[14]W. Horng, C. Lee, and C. Chen, “Classification of Age Groups Based on Facial Features,” Tamkang J. Sci. Eng., vol. 4, no. 3, pp. 183–192, 2001.

[15]I. Sobel, “An isotropic 3x3 image gradient operator,” in Machine Vision for Three – Dimensional Scenes, H. Freeman, Ed. New York: Academic Press, 1990, pp. 376–379.

[16]C. G. Looney, Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists, 1st ed. New York: Oxford University Press, 1997.

[17]A. Lanitis, C. Draganova, and C. Christodoulou, “Comparing Different Classifiers for Automatic Age Estimation,” IEEE Trans. Syst. Man, Cybern. Part B Cybern., vol. 34, no. 1, pp. 621–628, 2004.

[18]T. Cootes, G. Edwards, C. Taylor, H. Burkhardt, and B. Neumann, “Active appearance models,” in Computer Vision — ECCV’98, 1998, vol. 1407, pp. 484–498.

[19]T. Kohonen, Self Organizing Maps, 3rd ed. Berlin, Germany: Springer Berlin Heidelberg, 2001.

[20]X. Geng, Z.-H. Zhou, Y. Zhang, G. Li, and H. Dai, “Learning from facial aging patterns for automatic age estimation,” in Proceedings of the 14th annual ACM international conference on Multimedia - MULTIMEDIA ’06, 2006, p. 307.

[21]X. Geng, Z. Zhou, and K. Smith-miles, “Automatic Age Estimation Based on Facial Aging Patterns,” IEEE Trans. Image Process., vol. 29, no. 12, pp. 2234–2240, 2007.

[22]X. Luo, X. Pang, B. Ma, and F. Liu, “Age Estimation using Multi-Label Learning,” in 6th Chinese Conference, CCBR 2011, Beijing, China, December 3-4, 2011. Proceedings, 2011, pp. 221–228.

[23]D. Cao, Z. Lei, Z. Zhang, J. Feng, and S. Z. Li, “Human Age Estimation Using Ranking SVM,” in 7th Chinese Conference, CCBR, 2012, vol. 7701, pp. 324–331.

[24]R. Herbrich, T. Graepel, and K. Obermayer, “Large margin rank boundaries for ordinal regression,” in Advances in Large Margin Classifiers, 2000, pp. 115–132.

[25]R. Gross, I. Matthew, J. F. Cohn, T. Kanade, and S. Baker, “Multipie,” Image Vis. Comput., vol. 28, no. 5, pp. 807–813, 2010.

[26]T. M. Mitchell, Machine Learning. 1997.

[27]Y. F. Y. Fu, Y. X. Y. Xu, and T. S. Huang, “Estimating Human Age by Manifold Analysis of Face Pictures and Regression on Aging Features,” Multimed. Expo, 2007 IEEE Int. Conf., pp. 1383–1386, 2007.

[28]Y. Fu and T. S. Huang, “Human age estimation with regression on discriminative aging manifold,” IEEE Trans. Multimed., vol. 10, no. 4, pp. 578–584, 2008.

[29]S. Yan, X. Zhou, M. Hasegawa-johnson, and T. S. Huang, “Regression from Patch-Kernel,” in IEEE Conference on Computer Vision and Pattern Recognition, 2008, pp. 1–8.

[30]H. Takeda, S. Farsiu, and P. Milanfar, “Kernel Regression for Image Processing and Reconstruction,” IEEE Trans. Image Process., vol. 16, no. 2, pp. 349–366, 2007.

[31]K. Ricanek, Y. Wang, C. Chen, and S. J. Simmons, “Generalized Multi-Ethnic Face Age-Estimation,” in IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009, 2009.

[32]G. Guo and G. Mu, “Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 657–664.

[33]G. Guo and G. Mu, “Human Age Estimation: what is the influence across age and gender,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2010, pp. 71–78.

[34]S. Yan, H. Wang, T. S. Huang, Q. Yang, and X. Tang, “Ranking with Uncertain Labels,” in Multimedia and Expo, 2007 IEEE International Conference on, 2007, pp. 96–99.

[35]W. Chao, J. Liu, and J. Ding, “Facial age estimation based on label-sensitive learning and age-oriented regression,” Pattern Recognit., vol. 46, no. 3, pp. 628–641, 2013.

[36]O. F. W. Onifade and J. D. Akinyemi, “GWAgeER – A GroupWise Age Ranking Framework for Human Age Estimation,” Int. J. Image Graph. Signal Process., vol. 7, no. 5, pp. 1–12, 2015.

[37]D. Mining, Springer Series in Statistics The Elements of, 2nd ed., vol. 27, no. 2. Springer, 2009.

[38]G. Guo, Y. Fu, C. R. Dyer, and T. S. Huang, “A Probabilistic Fusion Approach to human age prediction,” in 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops, 2008.

[39]V. Vapnik, “Statistical Learning Theory,” in Adaptive and learning Systems for Signal Processing, Communications and Control, S. Haykin, Ed. New York: John Wiley & Sons Inc., 1998, pp. 1–740.

[40]G. G. G. Guo, G. M. G. Mu, Y. F. Y. Fu, C. Dyer, and T. Huang, “A study on automatic age estimation using a large database,” in Computer Vision, 2009 IEEE 12th International Conference on, 2009, vol. 12, pp. 1986–1991.

[41]M. Y. ElDib and M. El-saban, “Human Age Estimation Using Enhanced Bio-Inspired Features (EBIF),” in IEEE 17th International Conference on Image Processing, 2010, pp. 1589–1592.

[42]T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, “Active Shape Models-Their Training and Application,” Comput. Vis. Image Underst., vol. 61, no. 1, pp. 38–59, 1995.

[43]M. Y. ElDib and H. M. Onsi, “Human age estimation framework using different facial parts,” Egypt. Informatics J., vol. 12, no. 1, pp. 53–59, 2011.

[44]W. S. McCulloch and W. Pitts, “A Logical Calculus of the Idea Immanent in Nervous Activity,” Bull. Math. Biophys., vol. 5, no. 4, pp. 115–133, 1943.

[45]D. Kriesel, A Brief Introduction to Neural Networks. 2007.

[46]K. Chang, C. Chen, and Y. Hung, “A Ranking Approach for Human Age Estimation Based on Face,” in International Conference on Pattern Recognition, 2010, pp. 3396–3399.

[47]K. Chang, C. Chen, and Y. Hung, “Ordinal Hyperplanes Ranker with Cost Sensitivities for Age Estimation,” in IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 585 – 592.

[48]P. Yang, L. Zhong, and D. Metaxas, “Ranking Model for Facial Age Estimation,” in International Conference on Pattern Recognition, 2010, pp. 3408–3411.

[49]J. Suo, T. Wu, S. Zhu, S. Shan, X. Chen, and W. Gao, “Design sparse features for age estimation using hierarchical face model,” in 2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008, 2008, pp. 1–6.

[50]S. Yan, H. Wang, X. Tang, and T. S. Huang, “Learning auto-structured regressor from uncertain nonnegative labels,” in Proceedings of the IEEE International Conference on Computer Vision, 2007.

[51]Y. Freund and R. E. Schapire, “Experiments with a new boosting algorithm.,” in Proc. ICML, 1996, pp. 148–156.

[52]T. Cover and P. Hart, “Nearest neighbor pattern classiﬁcation,” IEEE Trans. Inf. Theory, vol. 13, no. 1, pp. 21–27, 1967.

[53]Y. Liang, X. Wang, L. Zhang, and Z. Wang, “A Hierarchical Framework for Facial Age Estimation,” Math. Probl. Eng., vol. 2014, pp. 1–8, 2014.

[54]B. Xiao, X. Yang, Y. Xu, and H. Zha, “Learning distance metric for regression by semideﬁnite programming with application to human age estimation,” in 17th ACM International Conference on Multimedia MM ’09, 2009, pp. 451–460.

[55]E. S. Choi, Y. J. Lee, J. S. Lee, K. R. Park, and J. Kim, “Age estimation using a hierarchical classifier based on global and local facial features,” Pattern Recognit., vol. 44, no. 6, pp. 1262–1281, 2011.

[56]J. Liu, Y. Ma, L. Duan, F. Wang, and Y. Liu, “Hybrid constraint SVR for facial age estimation,” Signal Processing, vol. 94, pp. 576–582, Jan. 2014.

[57]P. X. Gao, “Facial age estimation using Clustered Multi-task Support Vector Regression Machine,” in Proceedings - International Conference on Pattern Recognition, 2012, pp. 541–544.

[58]K. Luu, K. Seshadri, M. Savvides, T. D. Buil, and C. Y. Suenl, “Contourlet Appearance Model for Facial Age Estimation,” in International Joint Conference on Biometrics, 2011, pp. 1–8.

[59]E. Eidinger, R. Enbar, and T. Hassner, “Age and Gender Estimation of Unfiltered Faces,” IEEE Trans. Infromation Forenscis Secur., pp. 1–10, 2013.

[60]C. Shan, “Learning local features for age estimation on real-life faces,” Proc. 1st ACM Int. Work. Multimodal pervasive video Anal. - MPVA ’10, p. 23, 2010.

[61]H. Han and A. K. Jain, “Age, Gender and Race Estimation from Unconstrained Face Images,” East Lansing, Michigan, 2014.

[62]J. Ylioinas, A. Hadid, and M. Pietikäinen, “Age Classification in Unconstrained Conditions Using LBP Variants,” in International Conference on Pattern Recognition, 2012, pp. 1257–1260.