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An efficient face recognition system based on hybrid optimized KELM

  • S. Anantha PadmanabhanEmail author
  • Jayanna Kanchikere
Article
  • 7 Downloads

Abstract

Face recognition (FR) from video offers a challenging issue in the area of image exploration along with computer visualization, furthermore, as such recognized heaps of deem over the previous years on account of its numerous applications in the scope of domains. The chief challenges in the video centered FR are the restraint of the camera hardware, the random poses captured by means of the camera as the subject is noncooperative, and changes in the resolutions owing to disparate lighting conditions, noise along with blurriness. Numerous FR algorithms were generated in the previous decennium, although these approaches are much better, the image’s accuracy is less only. To trounce such difficulties, an efficient FR system centered on hybrid optimized Kernel ELM is proposed. The proposed work encompasses five phases, explicitly (i) preprocessing, (ii) Face detection, (iii) Feature Extraction, (iv) Feature Reduction, and (v) Classification. In the preliminary phase, the data-base video clips are converted in to the frames in which pre-processing are performed utilizing a Modified wiener filter to eliminate the noise. The succeeding phase is employed for detecting the pre-processed image via the viola–jones (V-J). With this technique, the face is identified. After that, the features are extorted. The extracted ones then will be provided as the input to the Modified PCA approach. Then, perform classification operation using hybrid (PSO-GA) optimized Kernel ELM approach. The similar process is replicated for query images (QI). At last, the recognized image is found. Experimental results contrasted with the previous ANFIS classifier and existing methods concerning precision, accuracy, recall, F-measure, sensitivity along with specificity. The proposed FR system indicates better accuracy when compared with the prevailing methods.

Keywords

Kernel extreme learning machine (KELM) Modified principal component analysis (MPCA) Hybrid particle swarm optimization-genetic algorithm (PSO-GA) Adaptive neuro-fuzzy inference system (ANFIS) Modified wiener filter (MWF) 

Notes

References

  1. 1.
    Agrawal S, Singh RK, Singh UP, Jain S (2018) Biogeography particle swarm optimization based counter propagation network for sketch based face recognition, Multimedia Tools and Applications, pp. 1–25Google Scholar
  2. 2.
    Alapati A, Kang D (2015) An efficient approach to face recognition using a modified center-symmetric local binary pattern (MCS-LBP). International Journal of Multimedia and Ubiquitous Engineering 10(8):13–22CrossRefGoogle Scholar
  3. 3.
    Alapati A, Kang D (2015) An efficient approach to face recognition using a modified center-symmetric local binary pattern (MCS-LBP). Int J Multimed Ubiquitos Eng 10(8):13–22CrossRefGoogle Scholar
  4. 4.
    Anupriya K, Gayathri R, Balaanand M, Sivaparthipan CB (2018) Eshopping scam identification using machine learning. International Conference on Soft-computing and Network Security (ICSNS), Coimbatore, India 2018:1–7.  https://doi.org/10.1109/ICSNS.2018.8573687 CrossRefGoogle Scholar
  5. 5.
    BalaAnand M, Karthikeyan N, Karthik S (2018) Designing a framework for communal software: based on the assessment using relation modelling. Int J Parallel Prog.  https://doi.org/10.1007/s10766-018-0598-2
  6. 6.
    BalaAnand M, Karthikeyan N, Karthick S, Sivaparthipan CB (2018) Demonetization: a visual exploration and pattern identification of people opinion on tweets. International Conference on Soft-computing and Network Security (ICSNS), Coimbatore, India 2018:1–7.  https://doi.org/10.1109/ICSNS.2018.8573616 CrossRefGoogle Scholar
  7. 7.
    BalaAnand M, Sankari S, Sowmipriya R, Sivaranjani S Identifying Fake User’s in Social Networks Using Non Verbal Behavior, International Journal of Technology and Engineering System (IJTES), Vol.7(2), pg:157–161Google Scholar
  8. 8.
    Cheng Y, Jin Z, Gao T, Chen H, Kasabov N (2016) An improved collaborative representation based classification with regularized least square (CRC-RLS) method for robust face recognition. Neurocomputing 215Google Scholar
  9. 9.
    Dadi HS, Pillutla GKM, Makkena ML (2017) Face recognition and human tracking using GMM, HOG and SVM in surveillance videos, Annals of Data Science, pp. 1–23Google Scholar
  10. 10.
    Hermosilla G, Rojas M, Mendoza J, Farías G, Pizarro FT, Martín CS, Vera E (2018) Particle swarm optimization for the fusion of thermal and visible descriptors in face recognition systems. IEEE Access 6:42800–42811CrossRefGoogle Scholar
  11. 11.
    Journal I, Trends C (2012) Study of musical influence on face using the local binary pattern ( LBP ) approach. Int J Comput Trends Technol 3:150–153Google Scholar
  12. 12.
    Kasar MM, Bhattacharyya D, Kim T (2016) ‘Face recognition using neural network : A review’, vol. 10, no. 3, pp. 81–100Google Scholar
  13. 13.
    Kavita M, Kau M (2016) A survey paper for face recognition technologies, International Journal of Scientific and Research Publications, vol. 6, no. 7Google Scholar
  14. 14.
    Li M, Yu C, Nian F, Li X (2015) ‘A face detection algorithm based on deep learning’, vol. 8, no. 11, pp. 285–296Google Scholar
  15. 15.
    Liu J, Liu W, Ma S, Wang M, Li L, Chen G (2018) Image-set based face recognition using k-svd dictionary learning, International Journal of Machine Learning and Cybernetics, pp. 1–14Google Scholar
  16. 16.
    Lu Z, Jiang X, Kot A (2018) Color space construction by optimizing luminance and chrominance components for face recognition, Pattern RecognitionGoogle Scholar
  17. 17.
    Maram B, Gnanasekar JM, Manogaran G et al (2018) SOCA.  https://doi.org/10.1007/s11761-018-0249-x
  18. 18.
    Mellal B (2012) A new approach for face recognition based on PCA & double LDA treatment combined with SVM. IOSR J Eng 2(4):685–691CrossRefGoogle Scholar
  19. 19.
    Nasution AL, Bima Sena Bayu D, Miura J (2014) Person identification by face recognition on portable device for teaching-aid system: Preliminary report, In Advanced Informatics: Concept, Theory and Application (ICAICTA), 2014 International Conference of, IEEE, pp. 171–176Google Scholar
  20. 20.
    Parmar DN, Brijesh B Mehta (2014) Face recognition methods & applicationsGoogle Scholar
  21. 21.
    Patinge PB (2015) Local binary pattern base face recognition system. Int J Sci Eng Technol Res 4(5):1356–1361Google Scholar
  22. 22.
    Sasirekha K, Thangavel K (2018) ‘Optimization of K-nearest neighbor using particle swarm optimization for face recognition’, Neural Computing and Applications, pp. 1–10Google Scholar
  23. 23.
    Shermina J (2010) Impact of locally linear regression and fisher linear discriminant analysis in pose invariant face recognition. International Journal of Computer Science and Network Security 10(10):111–115Google Scholar
  24. 24.
    Sukhija P, Behal S, Singh P (2016) Face recognition system using genetic algorithm. Procedia Computer Science 85:410–417CrossRefGoogle Scholar
  25. 25.
    Yang L, Zheng W, Cui Z, Zhang T (2018) Face recognition based on recurrent regression neural network, NeurocomputingGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of ECEGopalan College of Engineering and ManagementBangaloreIndia
  2. 2.Department of EEESt. Peters Engineering CollegeHyderabadIndia

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