Age Estimation is predicting a person’s age and is a very important attribute used for identity authentication. One of the major factors affecting the age estimation result is the identification of features of a person’s face accurately. Age Estimation has several real-world applications, equivalent to security management, biometrics, client relationship management, recreation, and cosmetology. The foremost ordinarily used age estimation technique is regression based mostly as a result of it takes into consideration the interrelationship among the age values for face pictures. The current work is an overview of techniques employed previously for age estimation.

Age Estimation, Forensics, Age based retrieval, Security, Surveillance, Label based learning, Label Distribution based learning

[1] Z.Hu and Y.Wen, Facial Age Estimation with Age Difference, IEEE TRANSACTIONS ON IMAGE PROCESSING,2016
[2] Z.A. Othman and D.A. Adnan, Age Classification from Facial Images System, International Journal of Computer Science and Mobile Computing,2014.
[3] V.G. Khetade and S. B. Thakare, An Efficient Method for Human Age Estimation by Label Distribution Learning,2014
[4] Karthikeyan D and Balakrishnan G, A comprehensive age estimation on face images using hybrid filter based feature extraction., Biomedical Research 2018.
[5] A.Sharma and S.Chhabra, A Hybrid Feature Extraction Technique for Face Recognition, International Journal of Advanced Research in Computer Science and Software Engineering,2017.
[6] Geng X, Yin C & Zhou ZH.” Facial age estimation by learning from label distributions.”, IEEE Trans Pattern Anal Mach Intell. 2013 Oct;35(10):2401-12. doi: 10.1109/TPAMI.2013.51.
[7] S.Soman and A.Austine, A Survey on Age Estimation Techniques, International Journal of Computer Applications ,2017.
[8] Shara M. S. and Shemitha P. A. A Survey on Facial Age Estimation Based on Multiple CNN , International Journal for Scientific Research & Development,2017.
[9] S.Soman & A.Austine,” A Survey on Age Estimation Techniques”, International Journal of Computer Applications, Volume 161 - Number 4,2017
[10] Xin Geng, Label Distribution Learning, IEEE TRANSACTIONS,2014
[11] W.Shen, K.Zhao, Y.Guo,and A.Yuille, Label Distribution Learning Forests, 31st Conference on Neural Information Processing Systems (NIPS 2017).
[12] G. Tsoumakas, M.-L. Zhang, and Z.-H. Zhou, “Tutorial on learning from multi-label data,” in European Conf. Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Bled, Slovenia, 2009.
[13] G. Tsoumakas and I. Katakis, “Multi-label classification: An overview,” Int’l Journal of Data Warehousing and Mining, vol. 3, no. 3, pp. 1–13, 2007.
[14] E. Hullermeier, J. F ¨ urnkranz, W. Cheng, and K. Brinker, “Label ranking ¨ by learning pairwise preferences,” Artif. Intell., vol. 172, no. 16-17, pp. 1897–1916, 2008
[15] P. Li, H. Li, and M. Wu, “Multi-label ensemble based on variable pairwise constraint projection,” Information Sciences, vol. 222, pp. 269– 281, 2013.
[16] J. Read, B. Pfahringer, G. Holmes, and E. Frank, “Classifier chains for multi-label classification,” Machine Learning, vol. 85, no. 3, pp. 333– 359, 2011.
[17] X. Geng, K. Smith-Miles, and Z. Zhou. Facial age estimation by learning from label distributions. In Proc. AAAI, 2010.
[18] A. L. Berger, S. D. Pietra, and V. J. D. Pietra. A maximum entropy approach to natural language processing. Computational Linguistics, 22(1):39–71, 1996.
[19] X. Geng, K. Smith-Miles, and Z. Zhou. Facial age estimation by learning from label distributions. In Proc. AAAI, 2010.
[20] X. Geng, C. Yin, and Z. Zhou. Facial age estimation by learning from label distributions. IEEE Trans. Pattern Anal. Mach. Intell., 35(10):2401–2412, 2013.
[21] X. Yang, X. Geng, and D. Zhou. Sparsity conditional energy label distribution learning for age estimation. In Proc. IJCAI, pages 2259–2265, 2016.
[22] X. Geng. Label distribution learning. IEEE Trans. Knowl. Data Eng., 28(7):1734–1748, 2016.
[23] X. Geng and P. Hou. Pre-release prediction of crowd opinion on movies by label distribution learning. In Pro. IJCAI, pages 3511–3517, 2015.
[24] C. Xing, X. Geng, and H. Xue. Logistic boosting regression for label distribution learning. In Proc. CVPR, pages 4489–4497, 2016.
[25] R.K.Shukla, A.Agarwal & A.K.Malviya,” An Introduction of Face Recognition and Face Detection for Blurred and Noisy Images”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.39-43 , June (2018).
[26] A. S.Banu, P.Vasuki, S. M.M.Roomi & A. Y.Khan,” SAR Image Classification by Wavelet Transform and Euclidean Distance with Shanon Index Measurement”, Journal (IJSRNSC) Vol.6 , Issue.3 , pp.13-17, Jun-2018.