Skip to main content
SpringerLink
Log in

PHT and KELM Based Face Recognition

  • Chapter
  • First Online:
Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough

Part of the book series: Studies in Computational Intelligence ((SCI,volume 885 ))

Abstract

Recognition of human face images is getting much attraction in pattern recognition since last few decades. Artificial intelligence and machine learning always tries to get more and more accurate for recognizing the face images. Only pixel based information of the face image can be helpful in recognizing the face images. This recognition rate can be increased if some feature of the face image is also added up with the pixel information of the face image. Based on this phenomenon, polar harmonic transform is utilized as the feature extraction technique for the feature based information. With this feature based information, kernel extreme learning machine (KELM) is utilized as the classification tool. It can be seen from the results obtained on the ORL, YALE and Georgia Tech face databases that more accurate results can be obtained using the feature based information.

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

Access this chapter

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 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

References

  1. Latha, P., Ganesan, L., Annadurai, S.: Face recognition using neural networks. Signal Process. Int. J. 3(5), 153–160 (2009)

    Google Scholar 

  2. Kim, K.I., Jung, K., Kim, H.J.: Face recognition using kernel principal component analysis. IEEE Signal Process. Lett. 9(2), 40–42 (2002)

    Article  Google Scholar 

  3. Goel, T., Nehra, V., Vishwakarma, V.P.: An adaptive non-symmetric fuzzy activation function-based extreme learning machines for face recognition. Arab. J. Sci. Eng. 42(2), 805–816 (2017)

    Article  Google Scholar 

  4. Lu, J., Liong, V.E., Wang, G., Moulin, P.: Joint feature learning for face recognition. IEEE Trans. Inf. Forensics Secur. 10(7), 1371–1383 (2015)

    Article  Google Scholar 

  5. Banitalebi-Dehkordi, M., Banitalebi-Dehkordi, A., Abouei, J., Plataniotis, K.N.: Face recognition using a new compressive sensing-based feature extraction method. Multimed. Tools Appl. 77(11), 14007–14027 (2018)

    Article  Google Scholar 

  6. Goel, A., Vishwakarma, V.P.: Fractional DCT and DWT hybridization based efficient feature extraction for gender classification. Pattern Recognit. Lett. 95, 8–13 (2017)

    Article  Google Scholar 

  7. Hafed, Z.M., Levine, M.D.: Face recognition using the discrete cosine transform. Int. J. Comput. Vis. 43(3), 167–188 (2001)

    Article  Google Scholar 

  8. Yang, J., Zhang, D., Frangi, A.F., Yang, J.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004)

    Article  Google Scholar 

  9. Li, L., Li, S., Abraham, A., Pan, J.-S.: Geometrically invariant image watermarking using polar harmonic transforms. Inf. Sci. (NY) 199, 1–19 (2012)

    Article  MathSciNet  Google Scholar 

  10. Qi, M., Li, B.-Z., Sun, H.: Image watermarking via fractional polar harmonic transforms. J. Electron. Imaging 24(1), 013004 (2015)

    Article  Google Scholar 

  11. Wang, X., et al.: Two-dimensional polar harmonic transforms for invariant image representation. IEEE Trans. Pattern Anal. Mach. Intell. 46(7), 403–418 (2010)

    Google Scholar 

  12. Huang, G.-B., Siew, C.-K.: Extreme learning machine with randomly assigned RBF kernels. Int. J. Inf. Technol. 11(1), 16–24 (2005)

    Google Scholar 

  13. Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE International Joint Conference on Neural Networks, 2004. Proceedings, vol. 2, 2004, pp. 985–990

    Google Scholar 

  14. Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B 42(2), 513–529 (2012)

    Article  Google Scholar 

  15. Wong, C.M., Vong, C.M., Wong, P.K., Cao, J.: Kernel-based multilayer extreme learning machines for representation learning. IEEE Trans. Neural Netw. Learn. Syst. (2016)

    Google Scholar 

  16. AT&T (ORL) face database: [Online]. Available: https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

  17. YALE face database: [Online]. Available: http://cvc.yale.edu/projects/yalefaces/yalefaces.%0Ahtml

  18. Georgia tech face database: [Online]. Available: http://ftp.ee.gatech.edu/pub/users/hayes/facedb/

  19. Yadav, S., Vishwakarma, V.P.: Interval type-2 fuzzy based pixel wise information extraction: an improved approach to face recognition. In: 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT), 2016, pp. 409–414

    Google Scholar 

  20. Xu, Y., Zhong, Z., Yang, J., You, J., Zhang, D.: A new discriminative sparse representation method for robust face recognition via l2 regularization. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2233–2242 (2017)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Dwarka Sector 16-C, New Delhi, 110078, India

    Sahil Dalal & Virendra P. Vishwakarma

Authors
  1. Sahil Dalal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Virendra P. Vishwakarma
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, Telangana, India

    Vinit Kumar Gunjan

  2. Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

  3. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

  4. Department of Computer Applications, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India

    G. R. Gangadharan

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Dalal, S., Vishwakarma, V.P. (2020). PHT and KELM Based Face Recognition. In: Gunjan, V., Zurada, J., Raman, B., Gangadharan, G. (eds) Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough. Studies in Computational Intelligence, vol 885 . Springer, Cham. https://doi.org/10.1007/978-3-030-38445-6_12

Download citation

Publish with us

Policies and ethics

Search

Navigation