Sreejit Panicker
Smita Selot
Editorial Notes
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ABSTRACT
Facial aging is a usual happening that is certain, and varies from person to person depending upon the circumstances and living habits. Applications of age determination are observed in domains like forensic science, security and also to determine health. Facial parameters used for age classification can be either structural or textural. In this paper we have used both approaches for feature extraction. In structural, facial growth is considered for classification, by computing the Euclidean distance between the various landmarks on facial image. The global features used to distinguish child from middle aged and adults is based on the ratios computed using the eyes, nose, mouth, chin, virtual-top of the head and the sides of the face as those features. In textural approach we consider skin texture for classifying the age groups. The prominent areas in facial skin are extracted where significant changes occur in terms of wrinkles that happen in the process of aging. Local Binary Pattern (LBP) feature is used for classification of age in different groups. The experimental results are significant and remarkable.
- Duong, Quach, Luu, le and karl. 2011. Fine Tuning Age-estimation with Global and Local Facial Features, IEEE Int. Conf. ICASSP (2011), 2032--35.Google Scholar
- Gu.Hua.,Su. Guangda., and Du. Cheng., 2003. Feature Points Extraction from Faces. Image and Vision Computing, Palmerston North, (2003), 154--158.Google Scholar
- Hewahi. N., Olwan. A., Tubeel. N., Asar.S. E. and Sultan. Z. A. 2010. Age Estimation based on Neural Networks using Face Features. Journal of Emerging Trends in Computing and Information Sciences. 1, 2(2010), 61--67.Google Scholar
- Jana, Datta and Saha. 2013. Age Group Estimation using Face Features. International Journal of Engineering and Innovative Technology (IJEIT), 3, 2 (2013), 130--134.Google Scholar
- Jana, Datta,and Rituparna Saha. 2014. Age Estimation from Face Image using Wrinkle Features (ICICT), Elsevier Procedia Computer Science, 46 (2015), 1754--1761.Google Scholar
- Lanitis. A., Draganova. C. and Christodoulou. C. 2004. Comparing Different Classifiers for Automatic Age Estimation, IEEE Transactions On Systems, Man, And Cybernetics---Part B: Cybernetics, 34, 1 (2004), 621--628. Google ScholarDigital Library
- Othman and Adnan. 2014.Age Classification from Facial Images System, IJCSMC, 3,10(2014), 291--303.Google Scholar
- Panicker, Smita Selot and Manisha Sharma. 2014. Human Age Estimation Through Face Synthesis: A Survey. i-manager's Journal on Pattern Recognition, 1,2(2014), 1--6.Google Scholar
- Ramanathan. N. and Chellappa. R. 2008. Modeling Shape and Textural Variations in Aging Faces. IEEE Int. Conf. automatic face and gesture recognition, Amsterdam, (2008), 1--8.Google Scholar
- Ramesha. K., Raja.K.B., Venugopal. K. R. and Patnaik L.M. 2010. Feature Extraction based Face Recognition, Gender and Age Classification. International Journal on Computer Science and Engineering, 02, 01(2010), 14--23.Google Scholar
- Suo. J., Zhu. S.C.,Shan. S. and Chen. X. 2010. A Compositional and Dynamic Model for Face Aging. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 3 (2010), 385--401. Google ScholarDigital Library
- Yen. G. G. and Nithianandan. N. 2002. Facial Feature Extraction using Genetic Algorithm. Congress on evolutionary computation, Honolulu, (2002), 1895--1900. Google ScholarDigital Library
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