Skip to main content

Advertisement

SpringerLink
Log in

A fast feature selection technique for real-time face detection using hybrid optimized region based convolutional neural network

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

Abstract

Today, face recognition research is popular owing to its potential applications, especially where privacy and security are involved. Many methods of deep learning can extract many complicated face features. Convolutional Neural Network (CNN) is normally used for face and image recognition. The CNN is a type of Artificial Neural Network (ANN) employing a convolution methodology that extracts features from input data for increasing the actual number of features. In this work, a Region-based Fully CNN (R-FCN) based framework for face detection is proposed. The R-FCN refers to a completely convolutional structure using a new position-sensitive pooling layer that extracts a score for the prediction of each such region. This helps in speeding up the network and sharing the computation of Region of Interests (RoIs), thus preventing the loss of information by the feature map in RoI-pooling. In this work, a hybrid Grammatical Evolution (GE) with a Grey Wolf Optimizer (GWO) (GE-GWO) algorithm has been proposed for optimizing the R-FCN structure to enhance face detection. The WIDER face dataset with a Face Detection Dataset and Benchmark (FDDB) was employed to evaluate techniques. The results have proved that the proposed technique achieves better performance (precision, recall, and ROC curve) than other existing methods in the range of 1.5–4.2%.

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

Similar content being viewed by others

References

  1. Alenazy WM, Alqahtani AS (2020) Improved crossover firefly algorithm based deep Beleif network for low-resolution face recognition. Multimedia Tools Appl: 1–21

  2. Arora S, Singh H, Sharma M, Sharma S, Anand P (2019) A new hybrid algorithm based on grey wolf optimization and crow search algorithm for unconstrained function optimization and feature selection. Ieee Access 7:26343–26361

    Article  Google Scholar 

  3. Arsenovic M, Sladojevic S, Anderla A, Stefanovic D (2017) FaceTime—deep learning based face recognition attendance system. In: 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY). IEEE, pp 000053–000058

  4. Chen Q, Shen F, Ding Y, Gong P, Tao Y, Wang J (2018) Face detection using R-FCN based deformable convolutional networks. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp 4165–4170

  5. Cho SW, Baek NR, Kim MC, Koo JH, Kim JH, Park KR (2018) Face detection in nighttime images using visible-light camera sensors with two-step faster region-based convolutional neural network. Sensors 18(9):2995

    Article  Google Scholar 

  6. Coşkun M, Uçar A, Yildirim Ö, Demir Y (2017) Face recognition based on convolutional neural network. In: 2017 International Conference on Modern Electrical and Energy Systems (MEES). IEEE, pp 376–379

  7. Kalaiarasi P (2021) Optimizing Convolutional Neural Network Using Particle Swarm Optimization For Face Recognition. Turkish J Comput Math Educ (TURCOMAT) 12(11):3672–3679

    Google Scholar 

  8. Kapoor K, Rani S, Kumar M, Chopra V, Brar GS (2021) Hybrid local phase quantization and grey wolf optimization based SVM for finger vein recognition. Multimedia Tools Appl 80(10):15233–15271

    Article  Google Scholar 

  9. Kumar D (2017) Feature selection for face recognition using DCT-PCA and Bat algorithm. Int J Inform Technol 9(4):411–423

    Article  Google Scholar 

  10. Kumar D, Kumar S, Rai CS (2009) Feature selection for face recognition: a memetic algorithmic approach. J Zhejiang University-Science A 10(8):1140–1152

    Article  MATH  Google Scholar 

  11. Lee JH, Kim DH, Jeong SN, Choi SH (2018) Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 77:106–111

    Article  Google Scholar 

  12. Li S (2020) Global face pose detection based on an improved PSO-SVM method. In: Proceedings of the 2020 international conference on aviation safety and information technology, pp 549–553

  13. Li H, Lin Z, Shen X, Brandt J, Hua G (2015) A convolutional neural network cascade for face detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5325–5334

  14. Ling H, Wu J, Huang J, Chen J, Li P (2020) Attention-based convolutional neural network for deep face recognition. Multimedia Tools Appl 79(9):5595–5616

    Article  Google Scholar 

  15. Mariani T, Guizzo G, Vergilio SR, Pozo AT (2016), July Grammatical evolution for the multi-objective integration and test order problem. In Proceedings of the Genetic and Evolutionary Computation Conference 2016 (pp. 1069–1076)

  16. Ramadan RM, Abdel-Kader RF (2009) Face recognition using particle swarm optimization-based selected features. Int J Signal Process Image Process Pattern Recognit 2(2):51–65

    Google Scholar 

  17. Sahoo AK, Pradhan C, Das H (2020) Performance evaluation of different machine learning methods and deep-learning based convolutional neural network for healthdecision making. Nature inspired computing for data science. Springer, Cham, pp 201–212

  18. Saremi S, Mirjalili SZ, &Mirjalili SM (2015) Evolutionary population dynamics and grey wolf optimizer. Neural Comput Appl 26(5):1257–1263

    Article  Google Scholar 

  19. Seal A, Ganguly S, Bhattacharjee D, Nasipuri M, Gonzalo-Martin C (2015) Feature selection using particle swarm optimization for thermal face recognition. In: Applied computation and security systems. Springer, New Delhi, pp 25–35

  20. Tawhid MA, Ali AF (2017) A hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function. Memetic Comput 9(4):347–359

    Article  Google Scholar 

  21. Tu S, ur Rehman S, Waqas M, ur Rehman O, Yang Z, Ahmad B, Zhao W (2020) Optimisation-based training of evolutionary convolution neural network for visual classification applications. IET Comput Vision 14(5):259–267

    Article  Google Scholar 

  22. Vignolo LD, Milone DH, Scharcanski J (2013) Feature selection for face recognition based on multi-objective evolutionary wrappers. Expert Syst Appl 40(13):5077–5084

    Article  Google Scholar 

  23. Yan H, Liu Y, Wang X, Li M, Li H (2021) A face detection method based on skin color features and AdaBoost algorithm. In Journal of Physics: Conference Series (Vol. 1748, No. 4, p. 042015). IOP Publishing

  24. Yang X, Liang N, Zhou W, Lu H (2020) A face detection method based on skin color model and improved AdaBoost algorithm. Traitement du Signal 37(6)

  25. Zhang S, Zhou Y (2015) Grey wolf optimizer based on powell local optimization method for clustering analysis. Discrete dynamics in nature and society

  26. Zhuang N, Yan Y, Chen S, Wang H, Shen C (2018) Multi-label learning based deep transfer neural network for facial attribute classification. Pattern Recogn 80:225–240

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Bharathiar University, Coimbatore, Tamilnadu, India

    D. T. T. Vijaya Kumar & R. Mahammad Shafi

Authors
  1. D. T. T. Vijaya Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Mahammad Shafi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. T. T. Vijaya Kumar.

Ethics declarations

Conflict of interest

Not applicable.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vijaya Kumar, D.T.T., Mahammad Shafi, R. A fast feature selection technique for real-time face detection using hybrid optimized region based convolutional neural network. Multimed Tools Appl 82, 13719–13732 (2023). https://doi.org/10.1007/s11042-022-13728-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13728-9

Keywords

Search

Navigation