Skip to main content
SpringerLink
Log in
Book cover

Soft Computing and Signal Processing pp 273–280Cite as

Motif Shape Primitives on Fibonacci Weighted Neighborhood Pattern for Age Classification

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 900))

Abstract

Age classification from facial images is increasingly receiving attention in age-based computer vision applications. To address this classification problem, the present paper proposes a new method of age grouping with motif shape primitives on the fibonacci weighted neighborhood pattern. FWNP on the image is computed, and motif shape primitives are evaluated on this FWNP image. These shape primitives are used for age variation of different persons. This method is investigated on facial image datasets of FG-NET database. The experimental study has shown the good performance of our proposed method against the other existing methods.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. R. Iga, K. Izumi, H. Hayashi, G. Fukano, T. Ohtani, A gender and age estimation system from face images, in SICE Annual Conference in Fukui, pp. 202–209 (2003)

    Google Scholar 

  2. A. Lanitis, On the significance of different facial parts for automatic age estimation, in 14th International Conference on Digital Signal Processing, vol. 2, pp. 1027–1030 (2002)

    Google Scholar 

  3. Y. Kwon, N. Lobo, Age classification from facial images. Comput. Vis. Image Underst. 74(1), 1–2 (1999)

    Article  Google Scholar 

  4. W. Horng, C. Lee, C. Chen, Classification of age groups based on facial features. Tamkang J. Sci. Eng. 4(3), 183–192 (2001)

    Google Scholar 

  5. Y.-H. Choi, K. Kim, E. Hwang, Classification based skin aging analysis, in International Asia-Pacific Web Conference, pp. 347–349 (2010)

    Google Scholar 

  6. F. Yun, X. Ye, T.S. Huang, Estimating human age by manifold analysis of face pictures and regression on aging features, in Proceeding of 2007 IEEE International Conference on Multimedia and Expo, pp. 1383–1386 (2007)

    Google Scholar 

  7. R. Nathan, R. Chellappa, Modeling age progression in young faces, in Proceeding of IEEE Conference on CVPR, vol. 1, pp. 387–394 (2006)

    Google Scholar 

  8. X. Geng, Z.-H. Zhou, Y. Zhang, G. Li, H. Dai, Learning from facial aging patterns for automatic age estimation, in Proceedings of the 14th Annual ACM International Conference on Multimedia, pp. 307–316 (2006)

    Google Scholar 

  9. A. Lanitis, Drag nova, and Christodoulou: comparing different classifiers for automatic age estimation. IEEE Trans. Syst. Man Cybern. Part B 34(l), 621–628 (2004)

    Article  Google Scholar 

  10. Y. Fu, T.S. Huang, Human age estimation with regression on the discriminative aging manifold. IEEE Trans. Multimedia 10(4), 578–584 (2008)

    Article  Google Scholar 

  11. A. Lanitis, C.J. Taylor, T.F. Cootes, Towards automatic simulation of aging effects on facial images. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 442–455 (2002)

    Google Scholar 

  12. S. Yan, T.S. Wang, T.S, Huang, X. Tang, Ranking with uncertain labels, in IEEE International Conference on Multimedia and Expo, pp. 96–99 (2007)

    Google Scholar 

  13. M. Hashemian, H. Pourghassem, Facial emotion processing in autism spectrum disorder based on spectral features of EEG signals. Int. J. Imaging Rob. 11(3), 68–80 (2013)

    Google Scholar 

  14. J. Ylioinas, A. Hadid, M. Pietikäinen, Age classification in unconstrained conditions using LBP variants, in Proceeding of ICPR, pp. 1257–1260 (2012)

    Google Scholar 

  15. P. Chandra Sekhar Reddy, Age classification with motif shape patterns on local binary pattern. Int. J. Comput. Trends Technol 39(3), 134–138 (2016)

    Google Scholar 

  16. P. Chandra Sekhar Reddy, Age classification with shape patterns derived from central pixel flooding matrix (CPFM) on facial images. Int. J. Latest Trends Eng. Technol. 7(4), 205–211 (2016)

    Google Scholar 

  17. P. Chandra Sekhar Reddy, B. Eswara Reddy V. Vijaya Kumar, New method for classification of age groups based on texture shape features. Int. J. Imaging Rob. (IJIR) 15(1), 19–30 (2015)

    Google Scholar 

  18. T. Ojala, M. Pietikäinen, D. Harwood, A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

  19. N. Jhanwar, S. Chaudhuri, G. Seetharaman, B. Zavidovique, Content-based image retrieval using motif co-occurrence matrix. Image Vision Comput. 22, 1211–1220 (2004)

    Article  Google Scholar 

  20. P. ChandraSekhar Reddy, B. Eswara Reddy, V. VijayaKumar, Texton based shape features on local binary pattern for age classification. Int. J. Image Graph. Signal Process. (IJIGSP) 4(7), 54–60 (2012)

    Article  Google Scholar 

  21. M. Chandra Mohan, V. Vijaya Kumar, B. Sujatha, Classification of child and adult based on geometric features of face using linear wavelets. Int. J. Signal Image Process. 1(3), 211–220 (2010)

    Google Scholar 

  22. T. Kanno, Classification of age group based on facial images of young males by using neural networks. IEICE Trans. Inf. Syst. 84(8) (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CSE Department, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India

    P. Chandra Sekhar Reddy, P. Vara Prasad Rao & M. Sridhar

  2. CSE Department, Malla Reddy Institute of Engineering and Technology, Hyderabad, India

    P. Kiran Kumar Reddy

Authors
  1. P. Chandra Sekhar Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Vara Prasad Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. Kiran Kumar Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Sridhar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Chandra Sekhar Reddy .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ, USA

    Jiacun Wang

  2. Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangaluru, Karnataka, India

    G. Ram Mohana Reddy

  3. Department of Computer Science and Engineering, JNTUH College of Engineering Hyderabad, Hyderabad, Telangana, India

    V. Kamakshi Prasad

  4. Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Secunderabad, Telangana, India

    V. Sivakumar Reddy

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chandra Sekhar Reddy, P., Vara Prasad Rao, P., Kiran Kumar Reddy, P., Sridhar, M. (2019). Motif Shape Primitives on Fibonacci Weighted Neighborhood Pattern for Age Classification. In: Wang, J., Reddy, G., Prasad, V., Reddy, V. (eds) Soft Computing and Signal Processing . Advances in Intelligent Systems and Computing, vol 900. Springer, Singapore. https://doi.org/10.1007/978-981-13-3600-3_26

Download citation

Publish with us

Policies and ethics

Search

Navigation