Age Prediction Using Facial Images

Reshma Zabeen K T*, V. Savithri**
* Student, Department of Computer Science and Technology, Women's Christian College, Chennai, India.
** Assistant Professor, Department of Computer Science and Technology, Women's Christian College, Chennai, India .
Periodicity:July - September'2017


Age estimation plays a vital role in human computer interaction where the image is given as input to the system after which, with the applied techniques the system provides result. An age group prediction system is estimated through AAM (Active Appearance Model) which calculates the texture and shape. A wrinkle is identified as a part of the shape information and the features, such as eye, nose, chin, lip, cheeks are extracted using PCA (Principle Component Analysis) of AAM, which then calculates the distance between the features and are stored as facial landmark points. The points are fed as input to the Mean Classification Algorithm which classifies based on two age groups, adult and old. Finally the Mean Absolute Error (MAE) value is estimated to determine the accuracy.


Age Group Identification, Active Appearace Model (AAM), Wrinkle Analysis, Facial Landmark Points, Mean Classification Algorithm

How to Cite this Article?

Zabeen, K.T.R. and Savithri, V. (2017). Age Prediction Using Facial Images. i-manager’s Journal on Image Processing, 4(3), 16-21.


[1]. Edwards, G. J., Cootes, T. F., & Taylor, C. J. (1998). Face recognition using active appearance models. In European Conference on Computer Vision (pp. 581- 595). Springer, Berlin, Heidelberg.
[2]. Fukai, H., Nishie, Y., Abiko, K., Mitsukura, Y., Fukumi, M., & Tanaka, M. (2008). An age estimation system on the aibo. In Control, Automation and Systems, 2008. ICCAS 2008. International Conference on (pp. 2551-2554). IEEE.
[3]. Geng, X., Zhou, Z. H., & Smith-Miles, K. (2007). Automatic age estimation based on facial aging patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12), 2234-2240.
[4]. Guo, G., Mu, G., Fu, Y., & Huang, T. S. (2009). Human age estimation using bio-inspired features. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on (pp. 112-119). IEEE.
[5]. Izadpanahi, S., & Toygar, O. (2012). Geometric feature based age classification using facial images. IET Conference on Image Processing (IPR 2012) (pp. 1-5).
[6]. Jana, R., Pal, H., & Chowdhury, A. R. (2012). Age Group Estimation using Face Angle. IOSR Journal of Computer Engineering (IOSRJCE), 7(5), 35-39.
[7]. Kwon, Y. H., & da Vitoria Lobo, N. (1999). Age classification from facial images. Computer Vision and Image Understanding, 74(1), 1-21.
[8]. Lanitis, A., Draganova, C., & Christodoulou, C. (2004). Comparing different classifiers for automatic age estimation. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(1), 621-628.
[9]. Malkauthekar, M. D., Sapkal, S. D., & Kakarwal, S. N. (2009). Experimental analysis of classification of facial images. In Advance Computing Conference, 2009. IACC 2009. IEEE International (pp. 1093-1098). IEEE.
[10]. Martínez, C., & Fuentes, O. (2003). Face recognition using unlabeled data. Computación y Sistemas, 7(2), 123-129.
[11]. Ramesha, K. B. R. K., Raja, K. B., Venugopal, K. R., & Patnaik, L. M. (2010). Feature extraction based face recognition, gender and age classification. International Journal on Computer Science and Engineering, 2(01S), 14-23.
[12]. Roy, H., Bhattacharjee, D., Nasipuri, M., & Basu, D. K. (2012). Age Range estimation from Human Face Images using Face Triangle Formation. International Journal of Research and Reviews in Information Sciences (IJRRIS), 2(1), 155-160.
[13]. Savithri, V., & Vani, P. (2016). Recognition Number Plate using ACA for Improved Segmentation and Classification. International Journal of Scientific & Engineering Research, 7(6), 803-807.
[14]. Shyamala, V., & Savithri, V. (2014). A Review of Study on Segmentation Methods. International Journal of Scientific & Engineering Research, 5(3), 1114-1116.
[15]. Zarit, B. D., Super, B. J., & Quek, F. K. (1999). Comparison of five color models in skin pixel classification. In Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 1999. Proceedings. International Workshop on (pp. 58-63). IEEE.

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