Skip to main content

Advertisement

SpringerLink
Log in

Interest point based face recognition using adaptive neuro fuzzy inference system

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

Abstract

In this paper, an efficient face recognition method using AGA and ANFIS-ABC has been proposed. At first stage, the face images gathered from the database are preprocessed. At Second stage, an interest point which is used to improve the detection rate consequently. The parameters used in the interest point determination are optimized using the Adaptive Genetic Algorithm. Finally using ANFIS, face images are classified by using extracted features. During the training process, the parameters of ANFIS are optimized using Artificial Bee Colony Algorithm (ABC) in order to improve the accuracy. The performance of the proposed ANFIS-ABC technique is evaluated using an ORL database with 400 images of 40 individuals, YALE-B database with 165 images of 15 individuals and finally with real time video the detection rate and false alarm rate is compared with proposed and existing methods to prove the system efficiency.

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  1. Agrawal V, Chandra S (2015) Feature selection using Artificial Bee Colony algorithm for medical image classification. In proceedings of International Conference on Contemporary computing

  2. Atharifard A, Ghofrani S (2011) Component-Based Face detection in Color Images. World Appl Sci J 13(4):847–857

    Google Scholar 

  3. Bong K et al (2018) A low-power convolutional neural network face recognition processor and a CIS integrated with always-on face detector. IEEE J Solid State Circuits 53(1):115–123

    Article  Google Scholar 

  4. Caleanu C-D, Botoca C (2007) C# Solutions for a Face Detection and Recognition System. Electronics and Energetics 20(1):93–105

    Google Scholar 

  5. Dagnes N et al (2017) 3D geometry-based automatic landmark localization in presence of facial occlusions. Multimedia Tools and Applications: 1–29

  6. Ghazali KHB, Ma J, Xiao R, Lubis SA (2012) An Innovative Face Detection Based on YCgCr Color Space. In: Proceedings of International Conference on Solid State Devices and Materials Science, Macao, pp. 2116–2124

  7. Goyani M, Joshi B, Shikkenawis G (2010) Acceptance / Rejection Rule Based Algorithm for Multiple Face Detection in Color Images. Int J Eng Sci Technol 2(6):2148–2154

    Google Scholar 

  8. Guler I, Ubeyli sE D (2005) Adaptive Neuro-Fuzzy Inference System for Classification of EEG Signals Using Wavelet Coefficients. J Neurosci Methods 148(2):113–121

    Article  Google Scholar 

  9. Haines N et al (2019) Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity. PLoS One 14(2):e0211735

    Article  Google Scholar 

  10. jain A, Chawla P, Goswami R, Bhargava A (2012) Color Images Based Face Detection Using Skin Likelihood Model and Morphological Operations. International Journal of Advanced Technology & Engineering Research 2(2):134–138

    Google Scholar 

  11. Kamath N, Holla A, Bhat S (2012) A Novel Face Detection and Tracking Algorithm in Real- Time Video Sequences. International Journal of Electronics Signals and Systems 2(1):25–28

    Google Scholar 

  12. Khaparde A, Reddy S, Ravipudi S (2010) Face detection using color based segmentation and morphological processing – a case study. The International Society of Certified Electronics Technicians

  13. Kuchi P, Gabbur P, Bhat S, David S (2002) Human Face Detection and Tracking using Skin Color Modeling and Connected Component Operators. IETE J Res 48(3):289–293

    Article  Google Scholar 

  14. Lakshmi V, Kulakarni P (2010) Segmentation Algorithm for Multiple Face Detection in Color Images with Skin Tone Regions using Color Spaces and Edge Detection Techniques. International Journal of Computer Theory and Engineering 2(4):1793–8201

    Google Scholar 

  15. Marcolin F et al (2017) Novel descriptors for geometrical 3D face analysis. Multimed Tools Appl 76(12):13805–13834

    Article  Google Scholar 

  16. Matsui A, Clippingdale S, Okabe N, Matsumoto T, Yagi N (2008) Bayesian Video Face Detection with Applications in Broadcasting. In: Proceedings of the 3rd Korea-Japan Joint Workshop on Pattern Recognition, Korea, pp. 95–96

  17. Moallema P, Mousavib BS, Monadjemic A (2011) A Novel Fuzzy Rule Base System for Pose Independent Faces Detection. Appl Soft Comput 11(2):1801–1810

    Article  Google Scholar 

  18. Nikisins O, Greitans M (2012) Local Binary Patterns and Neural Network based Technique for Robust Face Detection and Localization. In Proceedings of International Conference of the Biometrics Special Interest Group, Germany, pp. 271–276

  19. Pham The Bao (2006) Fast multi-face detection using facial component based validation by fuzzy logic. In: Proceedings of International Conference on Image Processing, Computer Vision, & Pattern Recognition, Las Vegas, USA, pp. 403–407

  20. Roy S, Podder S (2013) Face detection and its applications. International Journal of Research in Engineering & Advanced Technology 1(2):1–10

    Google Scholar 

  21. Shaaban Z (2011) Face Detection Methods. In: Proceedings of International Conference on Computer and Software Modeling, Singapore, pp. 53–56

  22. Sharma R, Patterh MS (2015) A new pose invariant face recognition system using PCA and ANFIS. Optik 126(23):3483–3487

    Article  Google Scholar 

  23. Shemshaki M, Amjadifard R (2011) Face Detection Using Fuzzy Granulation and Genetic algorithm In Color Images. In: proceeding of the 5th international conference on automation robotics and application, Wellington, New Zealand, pp. 244–249

  24. Soundararajan R, Biswas S (2019) Machine vision quality assessment for robust face detection. Signal Process Image Commun 72:92–104

    Article  Google Scholar 

  25. Subburaman VB, Marcel S (2013) Alternative Search Techniques for Face Detection Using Location Estimation and Binary Features. Comput Vis Image Underst 117(5):551–570

    Article  Google Scholar 

  26. Tripathi S, Sharma V, Sharma S (2011) Face Detection using Combined Skin Color. Int J Comput Appl 26(7):5–8

    Google Scholar 

  27. Vezzetti E, Marcolin F (2014) Geometry-based 3D faces morphology analysis: soft-tissue landmark formalization. Multimed Tools Appl 68(3):895–929

    Article  Google Scholar 

  28. Wang N-J, Chang S-C, Chou P-J (2012) A Real-time Multi-Face Detection System Implemented on FPGA. In: IEEE Proceedings of International Symposium on Intelligent Signal Processing and Communication Systems, Tamsui, pp. 333–337

  29. Xie S, Hu H (2019) Facial Expression Recognition Using Hierarchical Features With Deep Comprehensive Multipatches Aggregation Convolutional Neural Networks. IEEE Transactions on Multimedia 21(1):211–220

    Article  Google Scholar 

  30. Xu Y, Li Z, Yang J, Zhang D (2017) A survey of dictionary learning algorithms for face recognition. IEEE access 5:8502–8514

    Article  Google Scholar 

  31. Xu Y, Li Z, Zhang B, Yang J, You J (2017) Sample diversity, representation effectiveness and robust dictionary learning for face recognition. Inf Sci 375:171–182

    Article  Google Scholar 

  32. Yang M, Huang F, Lv X (2019) A feature learning approach for face recognition with robustness to noisy label based on top-N prediction. Neurocomputing 330:48–55

    Article  Google Scholar 

  33. Zeng N et al (2018) Facial expression recognition via learning deep sparse auto encoders. Neurocomputing 273:643–649

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Anna University, Nagercoil, Tamil Nadu, India

    Rejeesh M R

Authors
  1. Rejeesh M R
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rejeesh M R.

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

Rejeesh M R Interest point based face recognition using adaptive neuro fuzzy inference system. Multimed Tools Appl 78, 22691–22710 (2019). https://doi.org/10.1007/s11042-019-7577-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7577-5

Keywords

Search

Navigation