Skip to main content
SpringerLink
Log in

MFAST Processing Model for Occlusion and Illumination Invariant Facial Recognition

  • Conference paper
  • First Online:
Advanced Computing and Communication Technologies

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 452))

Abstract

Illumination Variation and wearable objects loses the partial facial information that it degrades the accuracy of recognition process. In this paper, a high performance driven accurate method is provided for facial recognition. The proposed MFAST (Multi-Featured Analog Signal Transformed) Model genuinely transmute the substantial facial information in analog featured conformation. This analog featured structured is formed using segmented featured elicitation. These features include center difference evaluation as moment, the asymmetric structure analysis as Skewness and Outlier Prone Measure as Kurtosis. These analogous features are shaped to justified form and generate a compound signal form. Mapping of these distillates signal points over facial dataset with specification of threshold window. The decomposed form recognition method enhanced the accuracy and performance. The experimentation on FERET, LFW and Indian Databases signify that the model outperformed than existing algorithms.

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Lee, M., Park, C.H.: An efficient image normalization method for face recognition under varying illuminations. In: Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval, pp. 128–133 (2008)

    Google Scholar 

  2. Kim, W., Suh, S., Hwang, W., Han, J.-J.: SVD face: illumination-invariant face representation. IEEE Signal Process. Lett. 21(11), 1336–1340 (2014)

    Article  Google Scholar 

  3. Han, Z., Fang, C., Ding, X.: A discriminated correlation classifier for face recognition. In: Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1485–1490 (2010)

    Google Scholar 

  4. Xu, Y., Fang, X., Yang, J., You, J., Liu, H., Teng, S.: Data uncertainty in face recognition. IEEE Trans. Cybern. 44(10), 1950–1961 (2014)

    Article  Google Scholar 

  5. Hu, H.: Face recognition with image sets using locally grassmannian discriminant analysis. IEEE Trans. Circuits Syst. Video Technol. 24(9), 1461–1474 (2014)

    Article  Google Scholar 

  6. Wan, J., Yang, M., Gao, Y., Chen, Y.: Pairwise costs in semisupervised discriminant analysis for face recognition. IEEE Trans. Inf. Forensics Secur. 9(10), 1569–1580 (2014)

    Article  Google Scholar 

  7. Kekre, H.B., Sarode, T.K., Natu, P.J., Natu, S.J.: Transform based face recognition with partial and full feature vector using DCT and walsh transform. In: International Conference and Workshop on Emerging Trends in Technology, pp. 1295–1300 (2011)

    Google Scholar 

  8. De Marsico, M., Nappi, M., Riccio, D.: Measuring sample distortions in face recognition. In: Proceedings of the 2nd ACM Workshop on Multimedia in Forensics, Security and Intelligence, pp. 83–88 (2010)

    Google Scholar 

  9. Rong, J., Gritti, T., Shan, C.: Upscaling faces for recognition systems using trained filters. In: Proceedings of the 1st International Workshop on Interactive Multimedia for Consumer Electronics, pp. 105–112 (2009)

    Google Scholar 

  10. Divya, R., Rath, A., Manikantan, K., Ramachandran, S.: Astroid shaped DCT feature extraction for enhanced face recognition. In: Proceedings of the CUBE International Information Technology Conference, pp. 95–101 (2012)

    Google Scholar 

  11. Wang, J., Lu, C., Wang, M., Yan, S., Hu, X.: Robust face recognition via adaptive sparse representation. IEEE Trans. Cybern. 44(12), 2368–2378 (2014)

    Article  Google Scholar 

  12. Xu, Y., Li, X., Yang, J., Lai, Z., Zhang, D.: Integrating conventional and inverse representation for face recognition. IEEE Trans. Cybern. 44(10), 1738–1746 (2014)

    Article  Google Scholar 

  13. Girgensohn, A., Adcock, J., Wilcox, L.: Leveraging face recognition technology to find and organize photos. In: Proceedings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 99–106 (2004)

    Google Scholar 

  14. Hu, S., Maschal, R., Young, S.S., Hong, T.H., Phillips, J.P.: Performance assessment of face recognition using super-resolution. In: Proceedings of the 10th Performance Metrics for Intelligent Systems Workshop, pp. 195–200 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. 307, Sector 14, Rohtak, 124001, Haryana, India

    Kapil Juneja

Authors
  1. Kapil Juneja
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kapil Juneja .

Editor information

Editors and Affiliations

  1. Asia Pacific Institute of Info. Tech., Director, Panipat, Haryana, India

    Ramesh K. Choudhary

  2. Department of Computer, Kalyani University, Kalyani, West Bengal, India

    Jyotsna Kumar Mandal

  3. IIT Ropar, Ropar, Punjab, India

    Nitin Auluck

  4. and Diagnostics, Centre for DNA Fingerprinting, Hyderabad, Andhra Pradesh, India

    H A Nagarajaram

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media Singapore

About this paper

Cite this paper

Juneja, K. (2016). MFAST Processing Model for Occlusion and Illumination Invariant Facial Recognition. In: Choudhary, R., Mandal, J., Auluck, N., Nagarajaram, H. (eds) Advanced Computing and Communication Technologies. Advances in Intelligent Systems and Computing, vol 452. Springer, Singapore. https://doi.org/10.1007/978-981-10-1023-1_16

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-1023-1_16

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-1021-7

  • Online ISBN: 978-981-10-1023-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation