AUTHORS: P. Tamije Selvy, G. Poorani, G. Sathya, P. Seethalakshmi, R. Swathi
Download as PDF
ABSTRACT: -Human facial age estimation is done using image processing. Many applications like forensics, security, and biometrics have attracted much attention in human facial age estimation. Multiclass classification and regression problem are the existing approaches that cast facial age estimation. We propose a positional ternary pattern algorithm that inherits the craniofacial shape with wrinkle and micro texture pattern. And then Gray-Level Co-occurrence Matrix plays a major role in revealing properties of gray levels in texture image. Age estimation based on human face remains a problem in computer vision and pattern recognition. To estimate an accurate age most of the existing system is used and it requires a huge data set attached with age labels. In addition to the proposed approach we proposed the probabilistic neural network that is widely used in classification and pattern recognition problem.
KEYWORDS: - Image processing, Positional Ternary Pattern, gray-Level Co-occurrence Matrix, Probabilistic Neural Network.REFERENCES:
 Y.Fu, G.Guo, and T.S.Huang,”Age sysnthesis and estimation via faces: A survey”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.32,no.11,pp. 1955- 1976,2010.
 W.B.Horng, C.P.Lee, and C.W.Chen,”Classification of age groups based on facial features,”Tamkang Journal of Science and Engineering, vol.4,no.3,pp.183-192,2001.
 LanitisC.J.Taylor, and T.F.Cootes,”Modeling the process of ageing in face images”, in Computer Vision, 1999. The proceedings of seventh IEEE international Conference on, vol.1.IEEE,1999,pp.131-136.
 K.Luu, K.Ricanek Jr, T.D.Bui, and C.Y.Suen, “Age estimation using active appearance models and support vector machine regression”, in Biometrics: theory, applications, and systems,2009. BTAS’09.IEEE 3rd International Conference on. IEEE,2009,pp.1-5.
 N.Ramanathan and R.Chellappa.Modeling Age Progression in Young Faces.CVPR,2006.
 Y.H.Kwon, and N.da Vitoria Lobo.Age classification from facial images.CVIU,74:1- 21,1999.
 J.Hayashi. Age and Gender Estimation Based on Wrinkle Texture and Color of Facial Images. Proceedings of the 16th International Conference,405-408,2002.
 T.Igarashi, K.Nishino and S.K.Nayar.The appearance of Human Skin. Technical Report,Dept.of ComputerScience, Columbia University CUCS-024-05,Jun,2005.
 A.Lanitis, C.Draganova, and C.Christodoulou.Comparing Different Classifiers for Automatic Age Estimation.IEEE Trans.SMCB,34(1):621-8,2004.
 Yun Fu, Guodong Guo, and Thomas S.Huang.Age synthesis and estimation via faces:A survey.IEEE Trans.PAMI, vol.11,pp.1955-1976,2010.
 Choi,Sung Eun,at al.”Age estimation using a hierarchical classifier based on global and local facial features.”Pattern Recognition,44(6):1262- 1281,2011
 A.Lanitis.On the significance of different facial parts for automatic age estimation.DSP,2002.
T.Ojala,M.pietikainen, and T.maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans.PAMI,vol.24,pp.971- 987,2002.
Turk M, Pentland A. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 1991; 3(1): p. 71– 86.
Belhumeur N, Hespantha JP, Kriegman D. Eigenfaces vs. Fisherfaces:Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and machine Intelligence, July 1997;19(7):p.711-720.
 Zarit D, Super BJ, Quel FKH. Comparison of five color models in skin pixel classification International Workshop on Recognition,Analysis and Tracking of Faces and Gestures in RealTime Systems, September1990;p.58- 63,Corfu,Greece.
 Hsu RL, Abdel-Mottaleb M, Jain AK.Face detection in color images. IEEE Transaction on Pattern Analysis and Machine Intelligence, May 2002; 24(5):p.696-706.
 Kwno YH Lobo NDV. Age Classification from Facial Images. Journal of Computer Vision and Image Understanding,1999; 74(1):p.1-21.
 Lanitis A, Taylor CJ, Cootes TF.Towards Automatic Simulation of Aging Effects on Face Images. IEEE Transaction on Pattern Analysis and Machine Intelligence,2002;24(4):p.442-455.
 Lanitis A, Draganova C, Christodoulou C. Comparing different classifiers for automatic age estimation. IEEE Transaction on Systems, Man and Cybernatics Part B: Cybernetics ,February 2004;34(1):p.621-628.
 Blanz V, Vetter T. Face recoginiton based on fitting a 3D morphable model. IEEE Transaction on Pattern Analysis and Machine Intelligence,September 2003; 25(9):p.1063- 1074.
 Kimmel R, Bronstein AM, Bronstein MM. Three-dimensional face recognition.International Journal of Computer Vision, August 2005; 64(1): p.5-30.
 Ramanathan N ,Chellappa R. Face verification across age progression . IEEE Conference on Computer Vision and Pattern Recognition, San Diego,CA, 2005;2:p.462-469.
 Ramanathan N, Chellappa R. Modelling Age Progression in young faces. IEEE Conference on Computer Vision and Pattern Recognition (CPVR),2006;1:p.387-394.
 Geng X, Zhou ZH, Smith-Miles K. Automatic age estimation based on facial aging patterns .IEEE Transaction on Pattern Analysis and Machine Intelligence,2007; 29:p.2234-2240.