Relationships Self-Learning Based Gender-Aware Age Estimation

Abstract

In biometrics research, face-appearance based age estimation (AE) becomes an important topic and has attracted a great deal of attention due to its potential applications. To achieve the goal of AE, a variety of methods have been proposed in the literature, among which the cumulative attribute (CA) coding based methods have achieved promising performance by preserving both ordinality and neighbor-similarity of ages. However, the sub-regressors responsible for regressing each of the CA coding elements are learned separately, while all of them are trained on the same dataset, leading to that potential correlation relationships of inter/intra-CA coding are not exploited. To this end, we herein propose a novel correlation learning method to model and utilize such inter/intra-CA relationships for AE, through self-learning from the training data. In addition, we extend the proposed method to perform gender-aware AE by further exploiting the correlations between and within gender groups. Furthermore, we introduce an alternating optimization algorithm for the proposed methods. Extensive experiments are conducted to demonstrate that the proposed methods can significantly improve the accuracy of AE, and more importantly that they can model well both inter/intra CA coding and gender relationships, regardless whether they are related (positive or negative) or not.

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Notes

  1. 1.

    In such case, we adopt the CA formulation in Eq. (2) to encode the \(y_{i}\) in Eq. (3).

  2. 2.

    Since in this work, we concentrate on exploring the correlations among the K-dimensional CA codings, so we define \(\Omega \in \mathbb {R}^{K\times K}\) as the column covariance matrix of the prior matrix-variate normal distribution on \(W \in \mathbb {R}^{d\times K}\), while introducing a d-order identity matrix \(I \in \mathbb {R}^{d\times d}\) as the row covariance matrix.

  3. 3.

    As reported in previous research [42, 44], the AAM, BIF and HoG visual features exhibit promising capacity in representing facial appearances as well as to evaluate the generalization ability of our method to feature representation variances, herein we extract various types of features from different face datasets for experiment.

  4. 4.

    \(MAE = \frac{1}{N}\sum _{i=1}^{N}|\widehat{l}_i-l_i|\) with \(l_i\) and \(\widehat{l}_i\) respectively being the actual and predicted age labels.

References

  1. 1.

    Guo G, Fu Y, Dyer CR, Huang TS (2008) Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Trans Image Process 17(7):1178–1188

    MathSciNet  Article  Google Scholar 

  2. 2.

    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 

  3. 3.

    Romano NC Jr, Fjermestad J (2006) Electronic customer relationship management. Electron Cust Relationsh Manag 3:1

    Google Scholar 

  4. 4.

    Jain AK, Dass SC, Nandakumar K (2004) Soft biometric traits for personal recognition systems. In: Biometric authentication. Springer, pp 731–738

  5. 5.

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

    Article  Google Scholar 

  6. 6.

    Ueki K, Hayashida T, Kobayashi T (2006) Subspace-based age-group classification using facial images under various lighting conditions. In: 7th International conference on automatic face and gesture recognition, 2006. FGR 2006. IEEE, pp 6–pp

  7. 7.

    Alnajar F, Shan C, Gevers T, Geusebroek J-M (2012) Learning-based encoding with soft assignment for age estimation under unconstrained imaging conditions. Image Vis Comput 30(12):946–953

    Article  Google Scholar 

  8. 8.

    Sai P-K, Wang J-G, Teoh E-K (2015) Facial age range estimation with extreme learning machines. Neurocomputing 149:364–372

    Article  Google Scholar 

  9. 9.

    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 

  10. 10.

    Fu Y, Xu Y, Huang TS (2007) Estimating human age by manifold analysis of face pictures and regression on aging features. In: IEEE international conference on multimedia and expo, 2007. IEEE, pp 1383–1386

  11. 11.

    Luu K, Ricanek K, Bui TD, Suen CY (2009) 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

  12. 12.

    Yan S, Wang H, Tang X, Huang TS (2007) Learning auto-structured regressor from uncertain nonnegative labels. In: IEEE 11th international conference on computer vision, 2007. ICCV 2007. IEEE, pp 1–8

  13. 13.

    Yan S, Wang H, Huang TS, Yang Q, Tang X (2007) Ranking with uncertain labels. In: IEEE international conference on multimedia and expo, 2007. IEEE, pp 96–99

  14. 14.

    Geng X, Zhou Z-H, 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 

  15. 15.

    Chang K-Y, Chen C-S, Hung Y-P (2011) Ordinal hyperplanes ranker with cost sensitivities for age estimation. In: IEEE conference on computer vision and pattern recognition (CVPR), 2011. IEEE, pp 585–592

  16. 16.

    Li C, Liu Q, Liu J, Lu H (2012) Learning distance metric regression for facial age estimation. In: 21st International conference on pattern recognition (ICPR), 2012. IEEE, pp 2327–2330

  17. 17.

    Li C, Liu Q, Liu J, Lu H (2012) Learning ordinal discriminative features for age estimation. In: IEEE conference on computer vision and pattern recognition (CVPR), 2012. IEEE, pp 2570–2577

  18. 18.

    Tian Q, Xue H, Qiao L (2016) Human age estimation by considering both the ordinality and similarity of ages. Neural Process Lett 43(2):505–521

    Article  Google Scholar 

  19. 19.

    Tian Q, Chen S (2017) Cross-heterogeneous-database age estimation through correlation representation learning. Neurocomputing 238:286–295

    Article  Google Scholar 

  20. 20.

    Guo G, Fu Y, Huang TS, Dyer CR (2008) Locally adjusted robust regression for human age estimation. In: IEEE workshop on applications of computer vision, 2008. WACV 2008. IEEE, pp 1–6

  21. 21.

    Kohli S, Prakash S, Gupta P (2013) Hierarchical age estimation with dissimilarity-based classification. Neurocomputing 120:164–176

    Article  Google Scholar 

  22. 22.

    Niu Z, Zhou M, Wang L, Gao X, Hua G (2016) Ordinal regression with multiple output cnn for age estimation. In: Computer vision and pattern recognition, pp 4920–4928

  23. 23.

    Yang X, Gao BB, Xing C, Huo ZW (2016) Deep label distribution learning for apparent age estimation. In: IEEE international conference on computer vision workshop, pp 344–350

  24. 24.

    Xing J, Li K, Hu W, Yuan C, Ling H (2017) Diagnosing deep learning models for high accuracy age estimation from a single image. Pattern Recognit 66:106–116

    Article  Google Scholar 

  25. 25.

    Liu H, Lu J, Feng J, Zhou J (2018) Label-sensitive deep metric learning for facial age estimation. IEEE Trans Inf Forensics Secur 13:292–305

    Article  Google Scholar 

  26. 26.

    Chen K, Gong S, Xiang T, Mary Q, Loy CC (2013) Cumulative attribute space for age and crowd density estimation. In: IEEE 26th international conference on pattern recognition, 2013. CVPR 2013. IEEE, pp 2467–2474

  27. 27.

    Li C, Liu Q, Liu J, Lu H (2015) Ordinal distance metric learning for image ranking. IEEE Trans Neural Netw Learn Syst 26(7):1551

    MathSciNet  Article  Google Scholar 

  28. 28.

    An S, Liu W, Venkatesh S (2007) Face recognition using kernel ridge regression. In: IEEE conference on computer vision and pattern recognition, 2007. CVPR’07. IEEE, pp 1–7

  29. 29.

    Tian Q, Chen S (2015) Cumulative attribute relation regularization learning for human age estimation. Neurocomputing 165:456–467

    Article  Google Scholar 

  30. 30.

    Coates A, Ng AY (2011) The importance of encoding versus training with sparse coding and vector quantization. In: International conference on machine learning, pp 921–928

  31. 31.

    Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222

    MathSciNet  Article  Google Scholar 

  32. 32.

    Montgomery DC, Peck EA, Vining GG (2012) Introduction to linear regression analysis, vol 821. Wiley, Hoboken

    Google Scholar 

  33. 33.

    Zhang Y, Yeung D-Y (2010) A convex formulation for learning task relationships in multi-task learning. In: IEEE international conference on uncertainty in artificial intelligence, pp 733–742

  34. 34.

    Liu M, Zhang D, Chen S, Xue H (2016) Joint binary classifier learning for ecoc-based multi-class classification. IEEE Trans Pattern Anal Mach Intell 38(11):2335–2341

    Article  Google Scholar 

  35. 35.

    Keerthi SS, Shevade SK (2014) Smo algorithm for least-squares svm formulations. Neural Comput 15(2):487–507

    Article  Google Scholar 

  36. 36.

    Guo G, Mu G (2010) Human age estimation: what is the influence across race and gender? In: Computer vision and pattern recognition workshops, pp 71–78

  37. 37.

    Vapnik VN (1998) Statistical learning theory. Wiley, New York

    Google Scholar 

  38. 38.

    Tian Q, Chen S (2018) Joint gender classification and age estimation by nearly orthogonalizing their semantic spaces. Image Vis Comput 69:9–21

    Article  Google Scholar 

  39. 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, 2006. FGR 2006. IEEE, pp 341–345

  40. 40.

    Mu G, Guo G, Fu Y, Huang TS (2009) Human age estimation using bio-inspired features. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009. IEEE, pp 112–119

  41. 41.

    Chen BC, Chen CS, Hsu WH (2015) Face recognition and retrieval using cross-age reference coding with cross-age celebrity dataset. IEEE Trans Multimed 17(6):804–815

    Article  Google Scholar 

  42. 42.

    Guo G, Mu G, Fu Y, Huang TS (2009) Human age estimation using bio-inspired features. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009. pp 112–119

  43. 43.

    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE computer society conference on computer vision and pattern recognition, pp 886–893

  44. 44.

    Guo G, Mu G, Fu Y, Dyer C, Huang T (2010) A study on automatic age estimation using a large database. In: IEEE international conference on computer vision, pp 1986–1991

  45. 45.

    Tu Y, Lin Y, Wang J, Kim J-U (2018) Semi-supervised learning with generative adversarial networks on digital signal modulation classification. Comput Mater Continua 55(2):243–254

    Google Scholar 

  46. 46.

    Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  47. 47.

    Wu C, Zapevalova E, Chen Y, Li F (2018) Time optimization of multiple knowledge transfers in the big data environment. Comput Mater Continua 54(3):269–285

    Google Scholar 

Download references

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China under Grants 61702273 and 61472186, the Natural Science Foundation of Jiangsu Province under Grant BK20170956, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant 17KJB520022, the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the Startup Foundation for Talents of Nanjing University of Information Science and Technology.

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Correspondence to Songcan Chen.

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Tian, Q., Cao, M., Chen, S. et al. Relationships Self-Learning Based Gender-Aware Age Estimation. Neural Process Lett 50, 2141–2160 (2019). https://doi.org/10.1007/s11063-019-09993-9

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Keywords

  • Age estimation
  • Cumulative attribute
  • Correlation learning strategy
  • Gender-aware age estimation