Skip to main content

Advertisement

Springer Nature Link
Log in

Deep neural network and 3D model for face recognition with multiple disturbing environments

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper presents the proposed bird search-based shuffled shepherd optimization algorithm (BSSSO) for face recognition. Initially, the input image undergoes a noise removal phase to eliminate noise in order to make them suitable for subsequent processing. The noise removal is performed using the type II fuzzy system and cuckoo search optimization algorithm (T2FCS), which detects noisy pixels from the image for improved processing. After the noise removal phase, the feature extraction is carried out using the convolution neural network (CNN) model and landmark enabled 3D morphable model (L3DMM). The obtained features are subjected to deep CNN for face recognition. The training of deep CNN is performed using the bird search-based shuffled shepherd optimization algorithm (BSSSO). Here, the proposed BSSSO is designed by combining the shuffled shepherd optimization algorithm (SSOA) and bird swarm algorithm (BSA) for inheriting the merits of both optimizations towards effective training of deep CNN. The proposed method obtained higher accuracy of 0.8935 and minimum FAR and FRR of 0.2190 and 0.2021 using LFW database with respect to training data.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Abdelmaksoud M, Nabil E, Farag I, Hameed HA (2020) A novel neural network method for face recognition with a single sample per person. IEEE Access 8:102212–102221. https://doi.org/10.1109/ACCESS.2020.2999030

    Article  Google Scholar 

  2. Ahsan MM, Li Y, Zhang J, Ahad MT, Munshi M, Yazdan S (2020) Face Recognition in an Unconstrained and Real-Time Environment Using Novel BMC-LBPH Methods Incorporates with DJI Vision Sensor. J Sensor Actuator Networks. 9(4):

  3. Arsenovic M, Sladojevic S, Anderla A, and Stefanoic D , (2017) “Face Time- Deep Learning Based Face Recognition Attendance System,” In Proceedings of IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY)

  4. Chen Z, Huang W, Lv Z (2017) Towards a face recognition method based on uncorrelated discriminant sparse preserving projection. Multimed Tools Appl 76:17669–17683

    Article  Google Scholar 

  5. Chen Z, Wu XJ, Yin HF, Kittler J (2020) Noise-robust dictionary learning with slack block-diagonal structure for face recognition. Pattern Recogn 100:107118

    Article  Google Scholar 

  6. Extended Yale B database, "http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html", Accessed on July 2020.

  7. Gao G, Yu Y, Yang M, Chang H, Huang P, Yue D (2020) Cross-resolution face recognition with pose variations via multilayer locality-constrained structural orthogonal procrustes regression. Inf Sci 506:19–36

    Article  MathSciNet  Google Scholar 

  8. Gulli A, Amita K, Sujit P (2019) Deep learning with TensorFlow 2 and Keras: regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API. Packt Publishing Ltd

  9. He M, Zhang J, Shan S, Kan M, Chen X (2020) Deformable face net for pose invariant face recognition. Pattern Recogn 100:107113

    Article  Google Scholar 

  10. Iranmanesh SM, Riggan B, Hu S, Nasrabadi NM (2020) Coupled generative adversarial network for heterogeneous face recognition. Image Vis Comput 94:103861

    Article  Google Scholar 

  11. Kapoor A (2019) Hands-On Artificial Intelligence for IoT: Expert machine learning and deep learning techniques for developing smarter IoT systems. Packt Publishing Ltd

  12. Kaveh A, Hamedani KB, Zaerreza A (2020) A set theoretical shuffled shepherd optimization algorithm for optimal design of cantilever retaining wall structures. Eng Comput:1–18

  13. Knyazev B, Shvetsov R, Efremova N, Kuharenko A (2018) “Leveraging large face recognition data for emotion classification” in proceedings of 13th IEEE international conference on automatic face and gesture recognition

  14. Kortli Y, Jridi M, Al Falou A, Atri M (2020) Face recognition systems: a survey. Sensors 20(2):342

    Article  Google Scholar 

  15. Kumar SV, Nagaraju C (2019) T2FCS filter: type 2 fuzzy and cuckoo search-based filter design for image restoration. J Vis Commun Image Represent 58:619–641

    Article  Google Scholar 

  16. Labeled Faces in the Wild Home database, "http://vis-www.cs.umass.edu/lfw/#download", Accessed on July 2020.

  17. Li XX, Hao P, He L, Feng Y (2020) Image gradient orientations embedded structural error coding for face recognition with occlusion. J Ambient Intell Humaniz Comput 11(6):2349–2367

    Article  Google Scholar 

  18. Li Y, Hu H, Zhu Z, Zhou G (2018) “CNNAuth: Continuous Authentication via Two-Stream Convolutional Neural Networks.” Proceed IEEE 13th Int Conf Networking, Archit Storage (NAS 2018). 1–9

  19. Li Y, Hu H, Zhu Z, Zhou G (2020) SCANet: Sensor-based Continuous Authentication with Two-stream Convolutional Neural Networks. ACM Trans Sensor Networks. 29

  20. Marsota M, Mrib J, Shanca X, Yee L, Fengd P, Yane X, Lie H, and Zhaoa Y (2020) An adaptive pig face recognition approach using Convolutional Neural Networks. Comput Electron Agricult. 173

  21. Meng XB, Gao XZ, Lu L, Liu Y, Zhang H (2016) A new bio-inspired optimisation algorithm: bird swarm algorithm. J Exp Theoretical Artif Intell 28(4):673–687

    Article  Google Scholar 

  22. Peng B, Wang W, Dong J and Tan T(2016) Automatic detection of 3d lighting inconsistencies via a facial landmark based morphable model. In proceedings of IEEE International Conference on Image Processing (ICIP). 3932–3936

  23. Prasad PS, Pathak R, Gunjan VK, Rao HR (2019) Deep learning based representation for face recognition. In ICCCE, Springer:419–424

  24. Soni N, Sharma EK, Kapoor A (2021) Hybrid meta-heuristic algorithm based deep neural network for face recognition. J Comput Sci 51:101352

  25. Soni N, et al (2019) Impact of artificial intelligence on businesses: from research, innovation, market deployment to future shifts in business models. arXiv preprint arXiv:1905.02092

  26. Soni N, et al (2018) Low-resolution image recognition using cloud Hopfield neural network. Progress in Advanced Computing and Intelligent Engineering. Springer, Singapore pp. 39–46.

  27. Soni N, et al (2016) Face recognition using cloud Hopfield neural network." 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE

  28. Soni N, et al (2018) Assistance System (AS) for Vehicles on Indian Roads: A Case Study." International Conference on Human Systems Engineering and Design: Future Trends and Applications. Springer, Cham

  29. The University of Milano Bicocca 3D Face Database, "http://www.ivl.disco.unimib.it/minisites/umbdb/request.html", Accessed on July 2020.

  30. Vishwakarma VP, Dalal S (2020) A novel non-linear modifier for adaptive illumination normalization for robust face recognition. Multimed Tools Appl:1–27

  31. Yan C, Xie H, Chen J, … Dai Q (2018) A fast Uyghur text detector for complex background images. IEEE Trans Multimedia 20:3389–3398

    Article  Google Scholar 

  32. Yang W, Zhang X, Li J (2020) A local multiple patterns feature descriptor for face recognition. Neurocomputing 373:109–122

    Article  Google Scholar 

  33. Zangeneh E, Rahmati M, Mohsenzadeh Y (2020) Low resolution face recognition using a two-branch deep convolutional neural network architecture. Expert Syst Appl 139:112854

    Article  Google Scholar 

Download references

Acknowledgements

One of the authors, Neha Soni, wants to thank the Department of Science & Technology (DST), Ministry of Science & Technology, New Delhi, India, for the financial support as DST Inspire Fellow.

Funding

This research received no external funding.

Author information

Authors and Affiliations

  1. Department of Electronic Science, University of Delhi South Campus, Delhi, India

    Neha Soni & Enakshi Khular Sharma

  2. Shaheed Rajguru College of Applied Sciences for Women, University of Delhi, Delhi, India

    Amita Kapoor

Authors
  1. Neha Soni
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Enakshi Khular Sharma
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Amita Kapoor
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Neha Soni.

Ethics declarations

Conflicts of Interest

The authors declare no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Soni, N., Sharma, E.K. & Kapoor, A. Deep neural network and 3D model for face recognition with multiple disturbing environments. Multimed Tools Appl 81, 25319–25343 (2022). https://doi.org/10.1007/s11042-022-12698-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12698-2

Keywords