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Fusion of Structural and Textural Facial Features for Generating Efficient Age Classifiers

Published: 25 August 2016 Publication History

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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.

References

[1]
Duong, Quach, Luu, le and karl. 2011. Fine Tuning Age-estimation with Global and Local Facial Features, IEEE Int. Conf. ICASSP (2011), 2032--35.
[2]
Gu.Hua.,Su. Guangda., and Du. Cheng., 2003. Feature Points Extraction from Faces. Image and Vision Computing, Palmerston North, (2003), 154--158.
[3]
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.
[4]
Jana, Datta and Saha. 2013. Age Group Estimation using Face Features. International Journal of Engineering and Innovative Technology (IJEIT), 3, 2 (2013), 130--134.
[5]
Jana, Datta,and Rituparna Saha. 2014. Age Estimation from Face Image using Wrinkle Features (ICICT), Elsevier Procedia Computer Science, 46 (2015), 1754--1761.
[6]
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.
[7]
Othman and Adnan. 2014.Age Classification from Facial Images System, IJCSMC, 3,10(2014), 291--303.
[8]
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.
[9]
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.
[10]
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.
[11]
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.
[12]
Yen. G. G. and Nithianandan. N. 2002. Facial Feature Extraction using Genetic Algorithm. Congress on evolutionary computation, Honolulu, (2002), 1895--1900.

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  • (2018)Use of Textural and Structural Facial Features in Generating Efficient Age ClassifiersRecent Findings in Intelligent Computing Techniques10.1007/978-981-10-8633-5_40(401-411)Online publication date: 4-Nov-2018

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cover image ACM Other conferences
ICIA-16: Proceedings of the International Conference on Informatics and Analytics
August 2016
868 pages
ISBN:9781450347563
DOI:10.1145/2980258
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Association for Computing Machinery

New York, NY, United States

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Published: 25 August 2016

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Author Tags

  1. Age Estimation
  2. Aging
  3. Texture

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  • (2018)Use of Textural and Structural Facial Features in Generating Efficient Age ClassifiersRecent Findings in Intelligent Computing Techniques10.1007/978-981-10-8633-5_40(401-411)Online publication date: 4-Nov-2018

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