Human Age Estimation by Considering both the Ordinality and Similarity of Ages

Neural Processing Letters

Volumn 43, Issue 2, 2016, Pages 505-521

  Tian, Qing ^a   Xue, Hui ^b   Qiao, Lishan ^c  

^a NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

^b SOUTHEAST UNIVERSITY   (China)

^c LIAOCHENG UNIVERSITY   (China)

Author keywords

Age estimation; Discriminant analysis; Least squares regression; Neighboring similarity; Ordinal relationship

Indexed keywords

DISCRIMINANT ANALYSIS; MEMBERSHIP FUNCTIONS; REGRESSION ANALYSIS;

AGE ESTIMATION; FACIAL APPEARANCE; HUMAN AGE ESTIMATION; LARGE MARGINS; LEAST SQUARES REGRESSION; NEIGHBORING SIMILARITY; ORDINAL REGRESSION; ORDINAL RELATIONSHIP;

LEAST SQUARES APPROXIMATIONS;

EID: 84926186929     PISSN: 13704621     EISSN: 1573773X     Source Type: Journal    
DOI: 10.1007/s11063-015-9423-8     Document Type: Article

References (30)

1. Agresti A (2010) Analysis of ordinal categorical data. Wiley, New York
2. Alnajar F, Shan C, Gevers T, Geusebroek JM (2012) Learning-based encoding with soft assignment for age estimation under unconstrained imaging conditions. Image Vis Comput 30:946–953
3. Bathe KJ, Wilson EL (1976) Numerical methods in finite element analysis. Prentice-Hall, Englewood Cliffs
4. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on computational learning theory. ACM, Pittsburgh, pp 144–152
5. Chen K, Gong S, Xiang T, Mary Q, Loy CC, (2013) Cumulative attribute space for age and crowd density estimation. In: IEEE conference on computer vision and pattern recognition CVPR 2013. IEEE, pp 2467–2474
6. Chu W, Keerthi SS (2005) New approaches to support vector ordinal regression. In: Proceedings of the 22nd international conference on machine learning. ACM, New York, pp 145–152
7. Cootes TF, Edwards GJ, Taylor CJ (1998) Active appearance models. Computer visionECCV98. Springer, Berlin, pp 484–498
8. Ding C, Choi J, Tao D, Davis LS (2014) Multi-directional multi-level dual-cross patterns for robust face recognition. arXiv preprint arXiv:1401.5311
9. Ding C, Tao D, (2015) A comprehensive survey on pose-invariant face recognition. arXiv preprint arXiv:1502.04383
10. Ding C, Xu C, Tao D (2015) Multi-task pose-invariant face recognition. IEEE Trans Image Process 24:980–993
11. 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. IEEE, pp 1383–1386
12. Gao F, Ai H (2009) Face age classification on consumer images with gabor feature and fuzzy lda method. Advances in biometrics. Springer, Berlin, pp 132–141
13. Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal chim Acta 185:1–17
14. Geng X, Yin C, Zhou ZH (2013) Facial age estimation by learning from label distributions. IEEE Trans Pattern Anal Mach Intell 35:2401–2412
15. 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 17:1178–1188
16. Jain AK, Dass SC, Nandakumar K (2004) Soft biometric traits for personal recognition systems. Biometric authentication. Springer, Berlin, pp 731–738
17. Lanitis A, Draganova C, Christodoulou C (2004) Comparing different classifiers for automatic age estimation. IEEE Trans Syst Man Cybern Part B 34:621–628
18. Lanitis A, Taylor CJ, Cootes TF (2002) Toward automatic simulation of aging effects on face images. IEEE Trans Pattern Anal Mach Intell 24:442–455
19. Li C, Liu Q, Liu J, Lu H (2012a) Learning distance metric regression for facial age estimation. In: 2012 21st international conference on pattern recognition (ICPR). IEEE, pp 2327–2330
20. Li C, Liu Q, Liu J, Lu H (2012b) Learning ordinal discriminative features for age estimation. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2570–2577
21. Li T, Zhu S, Ogihara M (2006) Using discriminant analysis for multi-class classification: an experimental investigation. Knowl Inf Syst 10:453–472
22. Liu J, Ma Y, Duan L, Wang F, Liu Y (2014) Hybrid constraint SVR for facial age estimation. Signal Process 94:576–582
23. 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
24. Mu G, Guo G, Fu Y, Huang TS (2009) Human age estimation using bio-inspired feature. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009. IEEE, pp 112–119
25. Fjermestad J, Romano N (2006) Electronic customer relationship management, vol 3. ME Sharpe
26. Sai PK, Wang JG, Teoh EK (2015) Facial age range estimation with extreme learning machines. Neurocomputing 149:364–372
27. Sun BY, Li J, Wu DD, Zhang XM, Li WB (2010) Kernel discriminant learning for ordinal regression. IEEE Trans Knowl Data Eng 22:906–910
28. 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, p 6
29. Vapnik VN (1998) Statistical learning theory. Wiley, New York
30. Zhang T, Zhou ZH (2013) Large margin distribution machine. arXiv preprint arXiv:1311.0989