Age Classification with Motif Shape Patterns on Local Binary Pattern
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2016 by IJCTT Journal|
|Year of Publication : 2016|
|Authors : P Chandra Sekhar Reddy, Bhanu Sreekar Reddy Karumuri|
|DOI : 10.14445/22312803/IJCTT-V39P123|
P Chandra Sekhar Reddy, Bhanu Sreekar Reddy Karumuri "Age Classification with Motif Shape Patterns on Local Binary Pattern". International Journal of Computer Trends and Technology (IJCTT) V39(3):134-138, September 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Age classification from facial images into different age groups is increasingly receiving attention in age based computer vision applications. Humans also cannot classify people into different age groupsprecisely. To address this problem, the present paper proposes an innovative method of agegroup classification based on motif shape patterns on thelocal binary pattern. LBP on theimage is computed and motif shape patterns are evaluated on this LBP image. The change of age of different persons can be observed with these shape patterns. The proposed method is evaluated on facial image datasets FG-Net and other scanned images. The experimental results demonstrate the excellent performance of our proposed method against the other existing methods.
 Iga, R., Izumi, K., Hayashi, H., Fukano, G., Ohtani, T.: A gender and age estimation system from face images. SICE Annual Conference in Fukui (2003) ,202–209.
 Lanitis, A.: On the significance of different facial parts for automatic age estimation. 14th International Conference on Digital Signal Processing 2 (2002) 1027–1030.
 Kwon, Y., Lobo, N.: Age classification from facial images. Computer Vision and Image Understanding 74(1) (1999) 1–21.
 Horng, W., Lee, C., Chen, C.: Classification of age groups based on facial features. Tamkang Journal of Science and Engineering 4(3) (2001) 183–192.
 Young-Hwan Choi , KyungrokKim, and Eenjun Hwang , “ classification based skin aging analysis”, International Asia-Pacific Web Conference, 2010, pp. 347-349.
 Yun, F., Ye, X. and Huang,2007, Estimating Human Age by Manifold Analysis of Face Pictures and Regression on Aging Features, Proc. of 2007 IEEE International Conference on Multimedia and Expo, pp. 1383-1386.
 Rama Nathan andChellappa R.,2006, Modeling age progression in young faces , proc. IEEE conf CVPR, vol.1, 387-394.
 Xin Geng, Zhi-Hua Zhou, Yu Zhang, Gang Li, Honghua Dai,2006, Learning from facial aging patterns for automatic age estimation Proceedings of the 14th annual ACM international conference on Multimedia,307-316.
 Lanitis, A. ,Draganova, and Christodoulou, 2004, Comparing different classifiers for automatic age estimation. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 34(l), 621-628.
 Fu Y., Huang, T.S,2008, Human age estimation with regression on thediscriminative aging manifold ,IEEE Tran multimedia, vol 10,4, 578-584.
 Lanitis, A. ,C Taylor, and Cootes ,2002, Towards automatic simulation of aging effects on facial images , IEEE Trans. Pattern Anal.macine intelligence Vol 24,4, 442-455.
 Yan,S., Wang, T.S, Huang and Tang X.,2007, Ranking with uncertain labels , IEEE International Conference on Multimedia and Expo,96-99,2007.
 Mina Hashemian, Hossein Pourghassem,2013, Facial Emotion Processing in Autism Spectrum Disorder Based on Spectral Features of EEG Signals, International Journal of Imaging and Robotics, Volume 11, Issue Number 3, 68-80.
 JuhaYlioinas, AbdenourHadid, MattiPietikäinen, 2012, Age Classification In Unconstrained Conditions Using LBP Variants, proc. ICPR 2012, 1257-1260.
 Vijaya Kumar V., JangalaSasiKiran and GortiSatyanarayana Murty,2013, Pattern Based Dimensionality Reduction Model For Age Classification, International Journal Of Computer Applications , Volume 79 – No 13,14-20.
 JangalaSasiKiran, Vijayakumar, V. and Eswara Reddy,B.,2013, Age Classifications Based on second Order Image Compressed and Fuzzy Reduced Grey Level (SICFRG) Model, International Journal On Computer Science And Engineering (IJCSE), Vol. 5 No. 06, 481-492.
 P.ChandraSekhar Reddy, B.Eswara Reddy and V.Vijaya Kumar, ”New Method for Classification of Age Groups Based on Texture Shape Features,” International Journal of Imaging and Robotics(IJIR), Volume 15(1), pp.19-30, 2015.
 T. Ojala, M. Pietikäinen, D. Harwood, A comparative study of texture measures with classification based on featured distributions, Pattern Recogn. 29 (1) (1996) 51–59.
 Jhanwar N, Chaudhuri S, Seetharaman G, Zavidovique B. Content-based image retrieval using motif co-occurrence matrix. Image Vision Computing 2004;22:1211–20.
P.ChandraSekhar Reddy, B. Eswara Reddy and V. VijayaKumar”Texton Based Shape Features on Local Binary Pattern forAge Classification,” International Journal of Image, Graphics and Signal Processing(IJIGSP), Volume4(7), pp.54-60, 2012.
 M. Chandra Mohan, V. Vijaya Kumar and B. Sujatha, “Classification of child and adult based on geometric features of face using linear wavelets”, International Journal of Signal and Image Processing, Vol. 1, No. 3, pp. 211 -220, 2010.
 Tsuneo Kanno, 2001, Classification of Age Group Based on Facial Images of Young Males by Using Neural Networks, IEICE Trans. Inf&Syst, VolE84-D, No 8.
Age classification, LBP, Motif shape patterns.