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Age classification with deep learning face representation

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Abstract

Automatic age estimation from facial images is challenging not only for computers, but also for humans in some cases. Therefore, coarse age groups such as children, teen age, adult and senior adult are considered in age classification, instead of evaluating specific age. In this paper, we propose an approach that provides a significant improvement in performance on benchmark databases and standard protocols for age classification. Our approach is based on deep learning techniques. We optimize the network architecture using the Deep IDentification-verification features, which are proved very efficient for face representation. After reducing the redundancy among the large number of output features, we apply different classifiers to classify the facial images to different age group with the final features. The experimental analysis shows that the proposed approach outperforms the reported state-of-the-arts on both constrained and unconstrained databases.

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References

  1. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: Application to face recognition. IEEE Trans Pattern Anal Mach Intell 28 (12):2037–2041

    Article  MATH  Google Scholar 

  2. Albert AM, Ricanek Jr K (2008) The morph database: Investigating the effects of adult craniofacial aging on automated face-recognition technology. Forensic Sci Commun 10(2). https://archives.fbi.gov/archives/about-us/lab/forensic-science-communications/fsc/april2008/research/2008_04_research02.htm

  3. Alley TR (2013) Social and applied aspects of perceiving faces. Psychology Press

  4. Azmi AN, Nasien D, Omar FS (2016) Biometric signature verification system based on freeman chain code and k-nearest neighbor. Multimedia Tools and Applications, pp 1–15

  5. Bishop CM (2006) Pattern recognition. Mach Learn 128

  6. Chang X, Yang Y (2016) Semisupervised feature analysis by mining correlations among multiple tasks. IEEE Transactions on Neural Networks and Learning Systems

  7. Choi SE, Lee YJ, Lee SJ, Park KR, Kim J (2011) Age estimation using a hierarchical classifier based on global and local facial features. Pattern Recogn 44(6):1262–1281

    Article  MATH  Google Scholar 

  8. Cootes TF, Edwards GJ, Taylor CJ et al (2001) Active appearance models. IEEE Trans Pattern Anal Machine Intell 23(6):681–685

    Article  Google Scholar 

  9. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  10. Ding S, Lin L, Wang G, Chao H (2015) Deep feature learning with relative distance comparison for person re-identification. Pattern Recogn 48(10):2993–3003

    Article  Google Scholar 

  11. Eidinger E, Enbar R, Hassner T (2014) Age and gender estimation of unfiltered faces. IEEE Trans Inf Forensic Secur 9(12):2170–2179

    Article  Google Scholar 

  12. FG-NET aging database. [online]. http://www.fgnet.rsunit.com/

  13. Fu Y, Xu Y, Huang TS (2007) Estimating human age by manifold analysis of face pictures and regression on aging features. In: 2007 IEEE International Conference on Multimedia and Expo, pp 1383–1386. IEEE

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

    Article  Google Scholar 

  15. Gao K, Lin S, Zhang Y, Tang S (2008) Object-based image retrieval with attention analysis and spatial re-ranking. In: International Conference on Intelligent Information Processing, pp 118–128. Springer

  16. Gao K, Zhang Y, Zhang W, Lin S (2010) Affine stable characteristic based sample expansion for object detection. In: Proceedings of the ACM International Conference on Image and Video Retrieval, pp 422–429. ACM

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

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

    Article  Google Scholar 

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

  20. Hayashi J, Yasumoto M, Ito H, Koshimizu H (2001) Method for estimating and modeling age and gender using facial image processing. In: 2001. Proceedings. Seventh International Conference on Virtual Systems and Multimedia, pp 439–448. IEEE

  21. Hayashi J, Yasumoto M, Ito H, Niwa Y, Koshimizu H (2002) Age and gender estimation from facial image processing. In: SICE 2002. Proceedings of the 41st SICE Annual Conference, vol 1, pp 13–18. IEEE

  22. Huang GB, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical report, Technical Report 07-49. University of Massachusetts, Amherst

    Google Scholar 

  23. Kanno T, Akiba M, Teramachi Y, Nagahashi H, Takeshi A (2001) Classification of age group based on facial images of young males by using neural networks. IEICE Trans Inf Syst 84(8):1094–1101

    Google Scholar 

  24. Kass M, Witkin A, Terzopoulos D (1988) Snakes: Active contour models. Int J Comput Vis 1(4):321–331

    Article  MATH  Google Scholar 

  25. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  26. Kwon YH, Da Vitoria Lobo N (1994) Age classification from facial images. In: 1994. Proceedings CVPR’94., 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp 762–767. IEEE

  27. Kwon YH, Da Vitoria Lobo N (1999) Age classification from facial images. Comput Vis Image Underst 74(1):1–21

    Article  Google Scholar 

  28. Lanitis A, Taylor CJ, Cootes TF (2002) Toward automatic simulation of aging effects on face images. IEEE Trans Pattern Anal Mach Intell 24(4):442–455

    Article  Google Scholar 

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

    Article  Google Scholar 

  30. Levi G, Hassner T (2015) Age and gender classification using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 34–42

  31. Liang Z, Ding S, Lin L (2015) Unconstrained facial landmark localization with backbone-branches fully-convolutional networks. arXiv:1507.03409

  32. Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image process 11 (4):467–476

    Article  Google Scholar 

  33. Liu G, Yan Y, Gao C, Tong W, Hauptmann A, Sebe N (2014) The mystery of faces: investigating face contribution for multimedia event detection. In: Proceedings of International Conference on Multimedia Retrieval, p 467. ACM

  34. Luo P, Wang X, Tang X (2012) Hierarchical face parsing via deep learning. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2480–2487. IEEE

  35. Marzec M, Koprowski R, Wróbel Z, Kleszcz A, Wilczyński S (2015) Automatic method for detection of characteristic areas in thermal face images. Multimed Tools Appl 74(12):4351–4368

    Article  Google Scholar 

  36. Patrick EA, Fischer F (1970) A generalized k-nearest neighbor rule. Inf Control 16(2):128–152

    Article  MathSciNet  MATH  Google Scholar 

  37. Peason K (1901) On lines and planes of closest fit to systems of point in space. Philos Mag 2:559–572

    Article  Google Scholar 

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

    Google Scholar 

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

  40. Schroff F, Kalenichenko D, Philbin J (2015) Facenet: A unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 815–823

  41. Seung HS, Lee DD (2000) The manifold ways of perception. Science 290 (5500):2268–2269

    Article  Google Scholar 

  42. Shan C (2010) Learning local features for age estimation on real-life faces. In: Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis, pp 23–28. ACM

  43. Sun Y, Wang X, Tang X (2013) Deep convolutional network cascade for facial point detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3476–3483

  44. Sun Y, Chen Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification. In: Advances in Neural Information Processing Systems, pp 1988–1996

  45. Toshev A, Szegedy C (2014) Deeppose: Human pose estimation via deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1653–1660

  46. Wang JG, Yau WY, Wang HL (2009) Age categorization via ecoc with fused gabor and lbp features. In: 2009 Workshop on Applications of Computer Vision (WACV), pp 1–6. IEEE

  47. Wolf L, Hassner T, Maoz I (2011) Face recognition in unconstrained videos with matched background similarity. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 529–534. IEEE

  48. Xie H, Gao K, Zhang Y, Li J (2011) Local geometric consistency constraint for image retrieval. In: 2011 18th IEEE International Conference on Image Processing, pp 101–104. IEEE

  49. Xiong X, De la Torre F (2013) Supervised descent method and its applications to face alignment. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 532–539

  50. Yan Y, Nie F, Li W, Gao C, Yang Y, Xu D (2016) Image classification by cross-media active learning with privileged information. IEEE Trans Multimed 18 (12):2494–2502

    Article  Google Scholar 

  51. Yan Y, Ricci E, Subramanian R, Liu G, Lanz O, Sebe N (2016) A multi-task learning framework for head pose estimation under target motion. IEEE Trans Pattern Anal Mach Intell 38(6):1070–1083

    Article  Google Scholar 

  52. Yang Z, Ai H (2007) Demographic classification with local binary patterns. In: International Conference on Biometrics, pp 464–473. Springer

  53. Yang Y, Nie F, Xu D, Luo J, Zhuang Y, Pan Y (2012) A multimedia retrieval framework based on semi-supervised ranking and relevance feedback. IEEE Trans Pattern Anal Mach Intell 34 (4):723–742

    Article  Google Scholar 

  54. Yang Y, Ma Z, Hauptmann AG, Sebe N (2013) Feature selection for multimedia analysis by sharing information among multiple tasks. IEEE Trans Multimed 15(3):661–669

    Article  Google Scholar 

  55. Zhang T (2011) Adaptive forward-backward greedy algorithm for learning sparse representations. IEEE Trans Inf Theory 57(7):4689–4708

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

The work is supported by the Science and Technology Planning Key Project of Guangdong Province, China (2015B010109003,2016A030303055, 2016B030305004), Natural Science Foundation of Guangdong Province, China (2015A030310509, 2016A030313437). Prof. Chen is supported by the Fundamental Research Funds for the Central Universities (Grant No. 2015ZZ029) and the Opening Project of Guangdong Province Key Laboratory of Big Data Analysis and Processing.

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Authors and Affiliations

  1. School of Computer Science, South China Normal University, Guangzhou, China

    Jin Huang & Jia Zhu

  2. School of Software Engineering, South China University of Technology, Guangzhou, China

    Bin Li & Jian Chen

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  1. Jin Huang
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  2. Bin Li
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  3. Jia Zhu
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  4. Jian Chen
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Correspondence to Bin Li or Jia Zhu.

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Huang, J., Li, B., Zhu, J. et al. Age classification with deep learning face representation. Multimed Tools Appl 76, 20231–20247 (2017). https://doi.org/10.1007/s11042-017-4646-5

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