Human Age Estimation Techniques Using Facial Features

This paper summarizes different techniques used for estimating the age of a human being using face detection. There are several facial features that differentiate individuals from one another. These features vary with the growth of a particular person. So using these features we can estimate the age of an individual. Using face recognition and identification of facial features, there are several techniques for age estimation that are briefly described in this paper. so are their financial requirements. This article focuses on customer satisfaction towards the quality of services offered by various banks.  A sample of 230 customers is taken for the survey using convenience sampling. A structured questionnaire is used to collect the data. Percentage analysis , chi-square and standard deviation are used to analyse the data. From the analysis it is found that most of the customers are satisfied with the services offered viz. Provision of savings account, ATM facility, different loans offered, online transaction facility, internet banking, sms facility etc. by the banks. However few customers expressed their dissatisfaction towards the credit card facility, too many formalities to issue a credit card, the credit period and credit limit

[1]  Zhi-Hua Zhou,et al.  Automatic Age Estimation Based on Facial Aging Patterns , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Timothy F. Cootes,et al.  Statistical models of face images - improving specificity , 1998, Image Vis. Comput..

[3]  H. K. Hussein.  Towards realistic facial modeling and re-rendering of human skin aging animation , 2002, Proceedings SMI. Shape Modeling International 2002.

[4]  Shuicheng Yan,et al.  Learning Auto-Structured Regressor from Uncertain Nonnegative Labels , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[5]  Hiroyasu Koshimizu,et al.  Age and gender estimation from facial image processing , 2002, Proceedings of the 41st SICE Annual Conference. SICE 2002..

[6]  C. Christodoulou,et al.  Comparing different classifiers for automatic age estimation , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  Haizhou Ai,et al.  Demographic Classification with Local Binary Patterns , 2007, ICB.

[8]  Bernard Tiddeman,et al.  A general method for overlap control in image warping , 2001, Comput. Graph..

[9]  Yun Fu,et al.  Locally Adjusted Robust Regression for Human Age Estimation , 2008, 2008 IEEE Workshop on Applications of Computer Vision.

[10]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Timothy F. Cootes,et al.  Toward Automatic Simulation of Aging Effects on Face Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Andreas Lanitis.  On the significance of different facial parts for automatic age estimation , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[14]  Hiroshi Nagahashi,et al.  Classification of Age Group Based on Facial Images of Young Males by Using Neural Networks , 2001 .

[15]  Niels da Vitoria Lobo,et al.  Age Classification from Facial Images , 1999, Comput. Vis. Image Underst..

[16]  H I Weber,et al.  Numerical modeling of facial aging. , 1998, Plastic and reconstructive surgery.

[17]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[18]  Christopher J. Solomon,et al.  Aging the human face - a statistically rigorous approach , 2005 .

[19]  Zicheng Liu,et al.  Image-based surface detail transfer , 2004, IEEE Computer Graphics and Applications.

[20]  Markus H. Gross,et al.  Simulating facial surgery using finite element models , 1996, SIGGRAPH.