Cross-heterogeneous-database age estimation through correlation representation learning

Neurocomputing

Volumn 238, Issue , 2017, Pages 286-295

  Tian, Qing ^a,b   Chen, Songcan ^b  

^a NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^b NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

Author keywords

Age estimation; Correlation component manifold space learning; Correlation representation learning; Cross heterogeneous database

Indexed keywords

DATABASE SYSTEMS;

AGE ESTIMATION; COMMODITY RECOMMENDATIONS; CORRELATION COMPONENTS; DISCRIMINATING ABILITIES; FEATURE REPRESENTATION; HETEROGENEOUS DATABASE; HETEROGENEOUS DATASETS; TRAINING AND TESTING;

STATISTICAL TESTS;

ADOLESCENT; ADULT; AGE DETERMINATION; AGE DISTRIBUTION; AGED; AGING; ARTICLE; CHILD; CONTROLLED STUDY; CORRELATION ANALYSIS; CORRELATION COMPONENT MANIFOLD SPACE LEARNING; CORRELATION REPRESENTATION LEARNING; CROSS HETEROGENEOUS DATABASE AGE ESTIMATION; DATA BASE; EXPERIMENTAL DESIGN; FEMALE; HUMAN; HUMAN EXPERIMENT; INFANT; INFORMATION PROCESSING; LEARNING ALGORITHM; MACHINE LEARNING; MALE; NORMAL HUMAN;

EID: 85012903188     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2017.01.064     Document Type: Article

References (42)

1. [1] Alnajar, F., Lou, Z., Alvarez, J., Gevers, T., Expression-invariant age estimation. Proceedings of the British Machine Vision Conference. BMVA Press, 2014.
2. [2] Alnajar, F., Shan, C., Gevers, T., Geusebroek, J.-M., Learning-based encoding with soft assignment for age estimation under unconstrained imaging conditions. Image Vis. Comput. 30:12 (2012), 946–953.
3. [3] Chang, K.-Y., Chen, C.-S., Hung, Y.-P., Ordinal hyperplanes ranker with cost sensitivities for age estimation. Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011, IEEE, 585–592.
4. [4] Chao, W.-L., Liu, J.-Z., Ding, J.-J., Facial age estimation based on label-sensitive learning and age-oriented regression. Pattern Recognit. 46:3 (2013), 628–641.
5. [5] Chen, K., Gong, S., Xiang, T., Loy, C., Cumulative attribute space for age and crowd density estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013, 2467–2474.
6. [6] Chen, Y.-Y., Cheng, A.-J., Hsu, W.H., Travel recommendation by mining people attributes and travel group types from community-contributed photos. IEEE Trans. Multim. 15:6 (2013), 1283–1295.
7. [7] Deng, Z., Choi, K.S., Jiang, Y., Wang, S., Generalized hidden-mapping ridge regression, knowledge-leveraged inductive transfer learning for neural networks, fuzzy systems and kernel methods. IEEE Trans. Cybern. 44:12 (2014), 2585–2599.
8. [8] Dibeklioglu, H., Alnajar, F., Ali Salah, A., Gevers, T., Combining facial dynamics with appearance for age estimation. IEEE Trans. Image Proces. 24:6 (2015), 1928–1943.
9. [9] Dibeklioglu, H., Salah, A.A., Gevers, T., Recognition of genuine smiles. IEEE Trans. Multim. 17:3 (2015), 279–294.
10. [10] Evgeniou, A., Pontil, M., Multi-task feature learning. Adv. Neural Inf. Process. Syst., 19, 2007, 41.
11. [11] Fu, Y., Huang, T.S., Human age estimation with regression on discriminative aging manifold. IEEE Trans. Multim. 10:4 (2008), 578–584.
12. [12] Fu, Y., Xu, Y., Huang, T.S., Estimating human age by manifold analysis of face pictures and regression on aging features. Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, 2007, IEEE, 1383–1386.
13. [13] Geng, X., Yin, C., Zhou, Z.-H., Facial age estimation by learning from label distributions. IEEE Trans. Pattern Anal. Mach. Intel. 35:10 (2013), 2401–2412.
14. [14] Geng, X., Zhou, Z.-H., Smith-Miles, K., Automatic age estimation based on facial aging patterns. IEEE Trans. Pattern Anal. Mach. Intel. 29:12 (2007), 2234–2240.
15. [15] Gu, B., Sheng, V.S., Tay, K.Y., Romano, W., Li, S., Incremental support vector learning for ordinal regression. IEEE Trans. Neural Netw. Learn. Syst. 26:7 (2015), 1403–1416.
16. [16] Gu, B., Sun, X., Sheng, V.S., Structural minimax probability machine. IEEE Trans. Neural Netw. Learn. Syst., 2016, 1–11.
17. [17] Gu, Q., Zhou, J., Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification. IEEE International Conference on Data Mining, 2009, IEEE, 159–168.
18. [18] Guo, G., Fu, Y., 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 (2008), 1178–1188.
19. [19] Jiang, Y., Deng, Z., Chung, F.L., Wang, S., Multi-task tsk fuzzy system modeling using inter-task correlation information. Inf. Sci., 298, 2015, 512ÿC533.
20. [20] Jin, X., Zhuang, F., Pan, S.J., Du, C., Luo, P., He, Q., Heterogeneous multi-task semantic feature learning for classification. Proceedings of the ACM International on Conference on Information and Knowledge Management, 2015, 1847–1850.
21. [21] Jolliffe, I., Principal Component Analysis. 2002, Wiley Online Library.
22. [22] Kohli, S., Prakash, S., Gupta, P., Hierarchical age estimation with dissimilarity-based classification. Neurocomputing 120 (2013), 164–176.
23. [23] Lanitis, A., Draganova, C., Christodoulou, C., Comparing different classifiers for automatic age estimation. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 34:1 (2004), 621–628.
24. [24] Lanitis, A., Taylor, C.J., Cootes, T.F., Toward automatic simulation of aging effects on face images. IEEE Trans. Pattern Anal. Mach. Intel. 24:4 (2002), 442–455.
25. [25] Li, C., Liu, Q., Liu, J., Learning ordinal discriminative features for age estimation. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, IEEE, 2570–2577.
26. [26] Li, C., Liu, Q., Liu, J., Lu, H., Learning distance metric regression for facial age estimation. Proceedings of the 2012 21st International Conference on Pattern Recognition (ICPR), 2012, IEEE, 2327–2330.
27. [27] Linoff, G.S., Berry, M.J., Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. 2011, John Wiley & Sons.
28. [28] Luu, K., Ricanek, K., Bui, T.D., Suen, C.Y., Age estimation using active appearance models and support vector machine regression. Proceedings of the IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, 2009. BTAS’09, 2009, IEEE, 1–5.
29. [29] Ni, B., Song, Z., Yan, S., Web image and video mining towards universal and robust age estimator. IEEE Trans. Multim. 13:6 (2011), 1217–1229.
30. [30] Palatucci, M., Pomerleau, D., Hinton, G.E., Mitchell, T.M., Zero-shot learning with semantic output codes. Advances in Neural Information Processing Systems, 2009, 1410–1418.
31. [31] Patterson, E., Sethuram, A., Albert, M., Ricanek, K., King, M., Aspects of age variation in facial morphology affecting biometrics. Rroceedings of the First IEEE International Conference on Biometrics: Theory, Applications, and Systems, 2007. BTAS 2007, 2007, IEEE, 1–6.
32. [32] Raab, G., Goddard, M.G.J., Ajami, R.A., Gargeya, V.B., Customer relationship management: A global perspective. 2012, Gower Publishing, Ltd.
33. [33] Sai, P.-K., Wang, J.-G., Teoh, E.-K., Facial age range estimation with extreme learning machines. Neurocomputing 149 (2015), 364–372.
34. [34] Su, Y., Fu, Y., Tian, Q., Gao, X., Cross-database age estimation based on transfer learning. Proceedings of the 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2010, IEEE, 1270–1273.
35. [35] Tian, Q., Xue, H., Qiao, L., Human age estimation by considering both the ordinality and similarity of ages. Neural Process. Lett. 43:2 (2016), 505–521.
36. [36] Tibshirani, R., Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodol.), 1996, 267–288.
37. [37] Ueki, K., Hayashida, T., Kobayashi, T., Subspace-based age-group classification using facial images under various lighting conditions. Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, 2006. FGR 2006, 2006, IEEE, 6–pp.
38. [38] Van Leeuwen, P., Lange, S., Klein, A., Geue, D., Grönemeyer, D.H., Dependency of magnetocardiographically determined fetal cardiac time intervals on gestational age, gender and postnatal biometrics in healthy pregnancies. BMC Pregnancy Childbirth, 4(1), 2004, 6.
39. [39] Wu, T.T., Lange, K., Coordinate descent algorithms for lasso penalized regression. Annals Appl. Stat., 2008, 224–244.
40. [40] Wu, Y., Wei, Z., Deng, H., et al. Attribute-based access to scalable media in cloud-assisted content sharing. IEEE Trans. Multim. 15:4 (2013), 778–788.
41. [41] Yan, S., Wang, H., Huang, T.S., Yang, Q., Tang, X., Ranking with uncertain labels. Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, 2007, IEEE, 96–99.
42. [42] Yan, S., Wang, H., Tang, X., Huang, T.S., Learning auto-structured regressor from uncertain nonnegative labels. Proceedings of the IEEE 11th International Conference on Computer Vision, 2007. ICCV 2007, 2007, IEEE, 1–8.