Age Estimation Based on CLM, Tree Mixture With Adaptive Neuron Fuzzy, Fuzzy Svm

Full Text (PDF, 420KB), PP.51-57

Views: 0 Downloads: 0


Mohammad Saber Iraji 1,* Mohammad Bagher Iraji 2 Alireza Iraji 2 Razieh Iraji 2

1. Department of Computer Engineering and Information Technology, Payame Noor University, I.R. of Iran

2. Department of Engineering, Damavand Branch, Islamic Azad University ,Science and Research Branch, Damavand ,Iran

* Corresponding author.


Received: 16 Oct. 2013 / Revised: 29 Nov. 2013 / Accepted: 7 Jan. 2014 / Published: 8 Feb. 2014

Index Terms

Face Age, AAM, CLM, Tree Mixture, Fuzzy Svm, Anfis


As you know, age diagnosis based on the image is one of the most attractive topics in computer .In this paper, we present a intelligent model to estimate the age of face image. We use shape and texture feature extraction from FG-NET landmark image data set using AAM(Active Appearance Model), CLM (Constrained Local Model), tree Mixture algorithms. Finally, the obtained features were given as the training data to the ANFIS (adaptive neuro fuzzy influence system), FSVM (Fuzzy Support Vector Machine). Our experimental results show that In our proposed system, fuzzy svm has less errors and system worked more accurate and appropriative than prior methods. Our system is able to identify age of face image from different directions as is.

Cite This Paper

Mohammad Saber Iraji, Mohammad Bagher Iraji, Alireza Iraji, Razieh Iraji,"Age Estimation Based on CLM, Tree Mixture With Adaptive Neuron Fuzzy, Fuzzy Svm ", IJIGSP, vol.6, no.3, pp.51-57, 2014. DOI: 10.5815/ijigsp.2014.03.07


[1]Sangeeta Agrawal, Rohit Raja, Sonu Agrawal," Support Vector Machine for age classification ", International Journal of Emerging Technology and Advanced Engineering Website: (ISSN 2250-2459, Volume 2, Issue 5, May 2012).

[2]D Cristinacce, TF Cootes ,"Feature Detection and Tracking with Constrained Local Modes", Proc. British Machine Vision Conference, Vol. 3, pp.929-938, 2006.

[3]Y.h.kwon and n.da Vitoria lobo, "locating facial features for age classification" , Proc. SPIE 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques, 62 (August 20, 1993).

[4]Khoa luu ,karl ricanek jr," Age Estimation using Active Appearance Models and Support Vector Machine Regression",IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, 2009.

[5]S. Yan, M. Liu, T. S. Huang, Extracting Age Information from Local Spatially Flexible Patches, ICASSP, 2008.

[6]A. Lanitis, C. J. Taylor, T. F. Cootes, Modeling the process of ageing in face images, ICCV, 1999.

[7]Sarah N. Kohail," Using Artificial Neural Network for Human Age Estimation Based on Facial Images", International Conference on Innovations in Information Technology (IIT), 2012.

[8]Hamid Moghadam fard, Sohrab Khanmohammadi, Sahraneh Ghaemi and Farshad Samadi," HUMAN AGE-GROUP ESTIMATION BASED ON ANFIS USING THE HOG AND LBP FEATURES", Electrical and Electronics Engineering: An International Journal (ELELIJ) Vol 2, No 1, February 2013.

[9]Hironobu Fukai_, Hironori Takimotoy, Yasue Mitsukura_ and Minoru Fukumi," Age and Gender Estimation by using Facial Image ", The 11th IEEE International Workshop on Advanced Motion Control March 21-24, 2010, Nagaoka, Japan.

[10]Feng Gao, Haizhou Ai," Face Age Classification on Consumer Images with Gabor Feature and Fuzzy LDA Method", ICB '09 Proceedings of the Third International Conference on Advances in biometrics,2009.

[11]Xiangxin zhu,deva ramanan," face detection, pose estimation, and landmark localization in the wild", Computer Vision and Pattern Recognition (CVPR), IEEE Conference on Biometrics Compendium, 2012.

[12]EvaggelosSpyrou, Giorgos Stamou, Yannis Avrithis and Stefanos Kollias," FUZZY SUPPORT VECTOR MACHINES FOR IMAGE CLASSIFICATION FUSING MPEG-7 VISUAL DESCRIPTORS", Integration of Knowledge, Semantics and Digital Media Technology, 2005. EWIMT 2005. The 2nd European Workshop on the (Ref. No. 2005/11099).

[13]Ashish Ghosh, B. Uma Shankar, Saroj K. Meher, "A novel approach to neuro-fuzzy classification", Neural Networks, Volume 22, Issue 1, January 2009, Pages 100-10 9.