Skip to main content
SpringerLink
Log in

Face Super Resolution by Tangential and Exponential Kernel Weighted Regression Model

  • Conference paper
  • First Online:
Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2 ( ICTIS 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 84))

Abstract

The need of recognizing individual from the low resolution non-frontal picture is hard hassle in video surveillance. In an effort to alleviate the hassle of popularity in low decision photograph, literature presents unique strategies for face recognition after converting the low decision photograph to excessive resolution. For this reason, this paper provides a method for multi-view face video notable decision using the tangential and exponential kernel weighted regression model. In this paper, a brand new hybrid kernel is proposed to carry out non-parametric kernel regression version for estimation of neighbor pixel within the first-rate decision after the face detection is done the usage of Viola-Jones algorithms. The experimentation is finished with the U.S. Face video databases and the quantitative results are analyzed the usage of the SDME with the prevailing strategies. From the result final results, we prove that the most SDME of 77.3 db is obtained for the proposed approach compared with the existing techniques like, nearest interpolation, bicubic interpolation and bilinear interpolation.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
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. Qu, S., Hu, R., Chen, S., Chen, L., Zhang M.: Robust face super-resolution via position-patch neighborhood preserving. In: Proceedings of IEEE International Conference on Multimedia and Expo Workshops (ICMEW), pp. 1–5 (2014)

    Google Scholar 

  2. Baker, S., Kanade, T.: Hallucinating faces. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 83–88 (2000)

    Google Scholar 

  3. Li, X., Hu, Y., Gao, X., Tao, D., Ning, B.: A multi-frame image super resolution method. Signal Process. 90, 405–414 (2010)

    Article  MATH  Google Scholar 

  4. Kim, K.I., Kown, Y.: Single-image super-resolution using sparse regression and natural image prior. IEEE Trans. Pattern Anal. Mach. Intell. 32(6), 127–1133 (2010)

    Google Scholar 

  5. Zhuang, Y., Zhang, J., Wu, F.: Hallucinatingfaces: LPH super-resolution and neighbor reconstruction for residue compensation. Pattern Recognit. 40, 3178–3194 (2007)

    Article  MATH  Google Scholar 

  6. Chang, H., Yeung, D., Xiong, Y.: Super-resolution through neighbor embedding. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 275–282 (2004)

    Google Scholar 

  7. Park, J., Lee, S.: An example-based face hallucination method for single-frame, low-resolution facial images. IEEE Trans. Image Process. 17, 1806–1816 (2008)

    Article  MathSciNet  Google Scholar 

  8. Farsiu, S., Robinson, M., Elad, M., Milanfar, P.: Fast and robust multiframe super-resolution. IEEE Trans. Image Process. 13, 1327–1344 (2004)

    Article  Google Scholar 

  9. Lu, T., Hu, R., Jiang, J., Zhang, Y., He, W.: Super-resolution for surveillance facial images via shape prior and residue compensation. Int. J. Multimedia Ubiquitous Eng. 8(6), 47–58 (2013)

    Article  Google Scholar 

  10. Tao, L., Ruimin, H., Zhen, H., Yang, X., Shang, G.: Surveillance face super-resolution via shape clustering and subspace learning. Int. J. Signal Process. Image Process. Pattern Recogn. 5(4), 107–116 (2012)

    Google Scholar 

  11. Jiang, J., Ruimin, H., Wang, Z., Han, Z.: Noise robust face hallucination via locality-constrained representation. IEEE Trans. Multimedia 16(5), 1268–1281 (2014)

    Article  Google Scholar 

  12. Takeda, H., Farsiu, S., Milanfar, P.: Kernel regression for image processing and reconstruction. IEEE Trans. Image Process. 16(2), 349–366 (2007)

    Article  MathSciNet  Google Scholar 

  13. Wand, M.P., Jones, M.C.: Kernel Smoothing, ser. Monographs on Statistics and Applied Probability. Chapman & Hall, New York (1995)

    Book  Google Scholar 

  14. Yee, P., Haykin, S.: Pattern classification as an ill-posed, inverse problem: a regularization approach. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 1, pp. 597–600 (1993)

    Google Scholar 

  15. Hu, Y., Lam, K.-M., Qiu, G., Shen, T.: From local pixel structure to global image super-resolution: a new face hallucination framework. IEEE Trans. Image Process. 20(2), 433–445 (2010)

    Article  MathSciNet  Google Scholar 

  16. Wang, X., Lin, H., Xu, X.: Parts-based face super-resolution via non-negative matrix factorization. Comput. Electr. Eng. 40(8), 130–141 (2014)

    Article  Google Scholar 

  17. Lu, T., Hu, R., Han, Z., Jiang, J., Zhang, Y.: From local representation to global face hallucination: a novel super-resolution method by nonnegative feature transformation. In: Proceedings of Visual Communications and Image Processing (VCIP), pp. 1–6 (2013)

    Google Scholar 

  18. Zeng, X., Huang, H.: Super-resolution method for multiview face recognition from a single image per person using nonlinear mappings on coherent features. IEEE Signal Process. Lett. 19(4), 195–198 (2012)

    Article  Google Scholar 

  19. Ma, X., Song, H., Qian, X.: Robust framework of single-frame face superresolution across head pose, facial expression, and illumination variations. IEEE Trans. Hum. Mach. Syst. 45(2), 238–250 (2015)

    Article  Google Scholar 

  20. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  21. Fleuret, F., Geman, D.: Coarse-to-fine face detection. Int. J. Comput. Vis. 41, 85–107 (2001)

    Article  MATH  Google Scholar 

  22. Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (1997)

    Google Scholar 

  23. Roth, D., Yang, M., Ahuja, N.: A snow based face detector. In: Neural Information Processing, vol. 12 (2000)

    Google Scholar 

  24. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Computational Learning Theory: Eurocolt 1995, pp. 23–37. Springer, New York (1995)

    Google Scholar 

  25. Panetta, K., Zhou, Y., Agaian, S., Jia, H.: Nonlinear unsharp masking for mammogram enhancement. IEEE Trans. Inf. Technol. Biomed. 15(6), 918–928 (2011)

    Article  Google Scholar 

  26. The UCSD face video database. http://vision.ucsd.edu/datasets/leekc/disk2/VideoDatabase/testing/

Download references

Author information

Authors and Affiliations

  1. Vignan University, Guntur, 522213, Andhra Pradesh, India

    B. Deshmukh Amar & N. Usha Rani

Authors
  1. B. Deshmukh Amar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Usha Rani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B. Deshmukh Amar .

Editor information

Editors and Affiliations

  1. Department of CSE, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Sabar Institute of Technology for Girls, Ahmedabad, Gujarat, India

    Amit Joshi

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Deshmukh Amar, B., Usha Rani, N. (2018). Face Super Resolution by Tangential and Exponential Kernel Weighted Regression Model. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2. ICTIS 2017. Smart Innovation, Systems and Technologies, vol 84. Springer, Cham. https://doi.org/10.1007/978-3-319-63645-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63645-0_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63644-3

  • Online ISBN: 978-3-319-63645-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation