Skip to main content

Advertisement

SpringerLink
Log in

An effective pose invariant face recognition system with the aid of ABC optimized ANFIS

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Utilizing a stocked up database of faces, Face recognition recognizes or verifies one or else more individuals in the specified still or else video images of a scene. This is owing to its abundant vital applications in security, human–computer interactions, authentication, and surveillance. A method intended for efficacy face recognition system on the video sequence is splashed by this paper. Deducting its intricacies is the salient cause of this paper. In the proposed posture invariant face recognition technique, first database video clip is separated into variant frames. Preprocessing is executed for every frame for removing the noise utilizing Gaussian filtering. The Face is identified from the preprocessed image by utilizing Viola–Jones algorithm. Then effective features are excerpted from detected face. Next, the excerpted feature value will present as input to train for adaptive neuro-fuzzy inference system (ANFIS) classifier. ANFIS parameters are optimized through artificial bee colony ABC algorithm to derive prompt high acknowledgment throughout the training. Likely the query image’s features are employed for verifying the suggested recognition system performance. The technique will be achieved on the working platform in MATLAB in addition the outcome will be examined and matched with the prevailing techniques for elucidating the suggested video face recognition method’s performance.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Sang, G., Li, J., Zhao, Q.: Pose-invariant face recognition via RGB-D images. Comput. Intell. Neurosci. 13, 9 (2016)

    Google Scholar 

  2. Chen, Q., Kotani, K., Lee, F., Ohmi, T.: An improved face recognition algorithm using histogram-based features in spatial and frequency domains. Int. J. Comput. Electr. Autom. Control Inf. Eng. 10(2), 360–364 (2016)

    Google Scholar 

  3. Sharma, N., Kaur, R.: Review of face recognition techniques. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 6(7), 20–24 (2016)

    Google Scholar 

  4. Ali, A.M.: A 3D-based pose invariant face recognition at a distance framework. IEEE Trans. Inf. Forensics Secur. 9(12), 2158–2169 (2014)

    Article  Google Scholar 

  5. Muruganantham, S., Jebarajan, T.: An efficient face recognition system based on the combination of pose invariant and illumination factors. Int. J. Comput. Appl. 50(2), 1–9 (2012)

    Google Scholar 

  6. Khadatkar, A., Khedgaonkar, R., Patnaik, K.S.: Occlusion invariant face recognition system. In: Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), pp. 1–4 (2016)

  7. Sindhuja, A., Devi Mahalakshmi, S., Vijayalakshmi, K.: Age invariant face recognition with occlusion. In: Advanced Communication Control and Computing Technologies (ICACCCT), pp. 83–87 (2012)

  8. Sharma, P., Yadav, R.N., Arya, K.V.: Pose-invariant face recognition using curvelet neural network. IET Biom. 3(3), 128–138 (2014)

    Article  Google Scholar 

  9. Ji, S., Xu, W., Yang, M., Yu, K.: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013)

    Article  Google Scholar 

  10. Drira, H., Amor, B.B., Srivastava, A., Daoudi, M.: Slama, R: 3D face recognition under expressions, occlusions, and pose variations. IEEE Trans. Pattern Anal. Mach. Intell. 35(9), 2270–2283 (2013)

    Article  Google Scholar 

  11. Sharma, R., Patterh, M.S.: A new hybrid approach using PCA for pose invariant face recognition. Wireless Pers. Commun. 85(3), 1561–1571 (2015)

    Article  Google Scholar 

  12. Beham, M.P., MansoorRoomi, S.M.: Face recognition using appearance based approach: a literature survey. In: Proceedings of International Conference & Workshop on Recent Trends in Technology, Mumbai, Maharashtra, India, vol. 2425 (2012)

  13. Meena, K., Suruliandi, A., Rose, R.R.: An illumination invariant texture based face recognition. ICTACT J. Image Video Process. 4(2), 709–716 (2013)

    Article  Google Scholar 

  14. Rath, S.K., Rautaray, S.S.: A survey on face detection and recognition techniques in different application domain. Int. J. Mod. Educ. Comput. Sci. 6(8), 34–44 (2014)

    Article  Google Scholar 

  15. Shermina, J.: Impact of locally linear regression and fisher linear discriminant analysis in pose invariant face recognition. Int. J. Comput. Sci. Netw. Secur. 10(10), 111–115 (2010)

    Google Scholar 

  16. Seshadri, K., Savvides, M.: Towards a unified framework for pose, expression, and occlusion tolerant automatic facial alignment. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 2110–2122 (2015)

    Article  Google Scholar 

  17. Amad, I.: Frenet frame-based generalized space curve representation for pose-invariant classification and recognition of 3-D face. IEEE Trans. Hum. Mach. Syst. 46(4), 522–533 (2016)

    Article  Google Scholar 

  18. Yang, J., Luo, L., Qian, J., Tai, Y., Zhang, F., Xu, Y.: Nuclear norm based matrix regression with applications to face recognition with occlusion and illumination changes. IEEE Trans. Pattern Anal. Mach. Intell. 39(1), 156–171 (2016)

    Article  Google Scholar 

  19. Tai, Y., Yang, J., Zhang, Y., Luo, L., Qian, J., Chen, Y.: Face recognition with pose variations and misalignment via orthogonal procrustes regression. IEEE Trans. Image Process. 25(6), 2673–2683 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  20. Drira, H., Ben-Amor, B., Srivastava, A., Daoudi, M., Slama, R.: 3D face recognition under expressions, occlusions, and pose variations. IEEE Trans. Pattern Anal. Mach. Intell. 35(9), 2270–2283 (2013)

    Article  Google Scholar 

  21. Oh, B.-S., Oh, K., Teoh, A.B.J., Lin, Z., Toh, K.-A.: A Gabor-based network for heterogeneous face recognition. Neuro Comput. 261, 253–265 (2017)

    Google Scholar 

  22. Biswas, S., Sil, J.: An efficient face recognition method using contourlet and curvelet transform. J. King Saud Univ. Comput. Inf. Sci. 1–12 (2017)

Download references

Author information

Authors and Affiliations

  1. Anna University, Chennai, India

    S. Anantha Padmanabhan

  2. SEA College of Engineering, Bangalore, India

    S. Anantha Padmanabhan & N. J. Krishna Kumar

Authors
  1. S. Anantha Padmanabhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. J. Krishna Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Anantha Padmanabhan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anantha Padmanabhan, S., Krishna Kumar, N.J. An effective pose invariant face recognition system with the aid of ABC optimized ANFIS. Cluster Comput 22 (Suppl 1), 177–187 (2019). https://doi.org/10.1007/s10586-018-2032-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2032-3

Keywords

Search

Navigation