Skip to main content
SpringerLink
Log in

Large Scale Double Density Dual Tree Complex Wavelet Transform Based Robust Feature Extraction for Face Recognition

  • Conference paper
  • First Online:
Proceedings of Second Doctoral Symposium on Computational Intelligence

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1374))

Abstract

Varying effects in face recognition often causes intrapersonal variations due to which efficient feature extraction is desirable. In this work, a significant feature extraction technique based on Double Density Dual Tree Complex Wavelet Transform (DD_DTCWT) is proposed for face recognition. DD_DTCWT is a variant of wavelet transformation which provides better multiresolution sub-band spectral analysis of face images with good shift invariance and directional selectivity. Extensive experiments are performed with slight pose variations on ORL database and on images with illumination and expression variations in YALE database. It has been depicted from experimental results that the proposed technique for large-scale Double Density Dual Tree Complex Wavelet Transform-based robust feature extraction yields accurate results on Yale database and better results on ORL database as compared with state-of-the-art techniques. The classification is performed on training and testing face vectors based on DD_DTCWT using K-nearest neighbor classifier. Several experiments are performed to analyze significant features by varying decomposition levels for sub-band selection and number of images in training set.

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 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

Institutional subscriptions

References

  1. Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld. (2003) Face recognition: a literature survey. ACM Computing Surveys, 35(4), 399–458.

    Google Scholar 

  2. Stan, Z. L., & Jain, A. (2005). In Handbook of face recognition, Springer.

    Google Scholar 

  3. Yadav, J., Rajpal, N., & Mehta, R. (2018). A new illumination normalization framework via homomorphic filtering and reflectance ratio in DWT domain for face recognition. Journal of Intelligent and Fuzzy Systems, 35, 5265–5277.

    Article  Google Scholar 

  4. Yadav, J., Rajpal, N., & Mehta, R. (2018). An improved hybrid illumination normalization and feature extraction model for face recognition. International Journal of Applied Pattern Recognition, 149–170.

    Google Scholar 

  5. Selvakumar, K., Jerome, J., & Rajamani, K. (2016) Robust face identification using DTCWT and PCA subspace based sparse representation. Multimedia Tools and Applications, 16073–16092.

    Google Scholar 

  6. Wang, J. W. et al. (2018). Illumination compensation for face recognition using adaptive singular value decomposition in the wavelet domain. Information Sciences, 435, 69–93.

    Google Scholar 

  7. Vishwakarma, V., & Dalal, S. (2020). A novel non-linear modifier for adaptive illumination normalization for robust face recognition. Multimedia Tools Applications, 79, 11503–11529.

    Article  Google Scholar 

  8. Lahaw, Z., Essaidani, D., & Seddik, H. (2018). Robust face recognition approaches using PCA, ICA, LDA based on DWT, and SVM algorithms. In 2018 41st International Conference on Telecommunications and Signal Processing (TSP) (pp. 1–5). Athens. https://doi.org/10.1109/TSP.2018.8441452

  9. Dalali, S., & Suresh, L. (2016). Daubechives wavelet based face recognition using modified lbp. . Procedia Computer Science, 93, 344–350.

    Article  Google Scholar 

  10. Huang, J., Zhang, Y., Zhang, H., & Cheng, K. (2019). Sparse representation face recognition based on gabor and CSLDP feature fusion. In 2019 Chinese Control and Decision Conference (CCDC) (pp. 5697–5701). Nanchang, China. https://doi.org/10.1109/CCDC.2019.8832457

  11. Rajpal, N., Singh, A., & Yadav, J. (2018). An expression invariant face recognition based on proximal support vector machine. In 2018 4th International Conference for Convergence in Technology (I2CT) (pp. 1–7). Mangalore, India. 1109/I2CT42659.2018.9058243

    Google Scholar 

  12. Wang, M., Jiang, H., & Li, Y. (2010). Face recognition based on DWT/DCT and SVM. In International Conference on Computer Application and System Modeling (ICCASM 2010) pp. 507–510. Taiyuan. https://doi.org/10.1109/ICCASM.2010.5620666

  13. Yadav, J., & Sehra, K. (2018). Large scale dual tree complex wavelet transform based robust features in PCA and SVD subspace for digital image watermarking. Procedia Computer Science, 132, 863–872. https://doi.org/10.1016/j.procs.2018.05.098

    Article  Google Scholar 

  14. Selesnick, I. (2004). The double-density dual-tree DWT. IEEE Transactions on Signal Processing, 52(5), 1304–1314. https://doi.org/10.1109/TSP.2004.826174

  15. Sharma, M., Sharma, P. et al. (2019). Double density dual-tree complex wavelet transform-based features for automated screening of knee-joint vibro-arthrographic signals. In Machine intelligence and signal analysis (pp. 279–290). Springer.

    Google Scholar 

  16. Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3, 71–86.

    Article  Google Scholar 

  17. Kathuria, D., & Yadav, J. (2016). An improved illumination invariant face recognition based on gabor wavelet transform. In Conference on Information and Communication Technology (CICT). IEEE.

    Google Scholar 

  18. Jyotsna, R., N., & Vishwakarma, V. (2016). Face recognition using Symlet, PCA and Cosine angle distance measure. In Ninth International Conference on Contemporary Computing, IEEE.

    Google Scholar 

  19. Bagherzadeh, S., Sarcheshmeh, A. et al. (2016). A new hybrid face recognition algorithm based on discrete wavelet transform and direct LDA. In 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME) (pp. 267–270). Tehranhttps://doi.org/10.1109/ICBME.2016.789096

  20. Yan, X. (2016) Modular 2DPCA face recognition algorithm based on image segmentation. In IEEE International Conference on Signal and Image Processing (ICSIP) (pp. 210–213). Beijing. https://doi.org/10.1109/SIPROCESS.2016.7888254

  21. Maw, H., Thu, S., & Mon, M. (2019). Face recognition based on illumination invariant techniques model. In International Conference on Advanced Information Technologies (ICAIT) (pp. 120–125). Yangon, Myanmar. https://doi.org/10.1109/AITC.2019.8921027

  22. Ouyang, A., Liu, Y., Pei, S., et al. (2020). A hybrid improved kernel LDA and PNN algorithm for efficient face recognition. Neurocomputing, 14(393), 214–222.

    Article  Google Scholar 

  23. Liang, J., Hou, Z., Chen, C. et al. (2016). Supervised bilateral two-dimensional locality preserving projection algorithm based on Gabor wavelet. SIViP, 10, 1441–1448. https://doi.org/10.1007/s11760-016-0950-1

  24. Rangsee, P., Raja, K., & Venugopal, K. (2018). modified local ternary pattern based face recognition using SVM. In International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) (pp. 343–350). Bangkok. https://doi.org/10.1109/ICIIBMS.2018.8549952

  25. Li, M., Yu, X., Ryu, K., et al. (2018). Face recognition technology development with Gabor, PCA and SVM methodology under illumination normalization condition. Cluster Computing, 21, 1117–1126. https://doi.org/10.1007/s10586-017-0806-7

    Article  Google Scholar 

  26. Yadav, J., & Mehta, R. (2019). An improved illumination normalization and robust feature extraction technique for face recognition under varying illuminations. Arabian Journal for Science and Engineering, 44(11), 9067–9086.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Dwarka, New Delhi, India

    Juhi Chaudhary & Jyotsna Yadav

Authors
  1. Juhi Chaudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jyotsna Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jyotsna Yadav .

Editor information

Editors and Affiliations

  1. Department of Computer Science Engineering, Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Deepak Gupta

  2. Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Ashish Khanna

  3. Institute of Engineering and Technology, Lucknow, Uttar Pradesh, India

    Vineet Kansal

  4. University of Calabria, Rende, Cosenza, Italy

    Giancarlo Fortino

  5. Department of Information Technology, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chaudhary, J., Yadav, J. (2022). Large Scale Double Density Dual Tree Complex Wavelet Transform Based Robust Feature Extraction for Face Recognition. In: Gupta, D., Khanna, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Second Doctoral Symposium on Computational Intelligence . Advances in Intelligent Systems and Computing, vol 1374. Springer, Singapore. https://doi.org/10.1007/978-981-16-3346-1_33

Download citation

Publish with us

Policies and ethics

Search

Navigation