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HOG Based Facial Recognition Approach Using Viola Jones Algorithm and Extreme Learning Machine

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Computational Science and Its Applications – ICCSA 2019 (ICCSA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11623))

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Abstract

Extreme Learning Machine has attracted widespread attention for its exemplary performance in solving regression and classification problems. It is a type of single layer feed-forward neural machine which relies on randomly allocating the input weights and hidden layer biases. Through this, the ELM has been found to possess running time spans which are within millisecond regime. It does not require complex controlling parameters which makes its implementation elementary. This paper investigates the performance of employing Extreme Learning Machine as a classifier to be used for the face recognition problem. Viola Jones algorithm has been employed to detect and extract the faces from the dataset. Finally, Histogram of Oriented Gradients (HOG) features are extracted which form the basis of classification. The scheme so presented has been tested on standard face recognition datasets from AT&T and YALE. The resulting training/testing time spans of the whole scheme range from milliseconds to seconds, dictating the compatibility of ELM with real-time events.

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References

  1. Zhao, W., Chellappa, R., Philips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003)

    Article  Google Scholar 

  2. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1992). Massachusetts Institute of Technology

    Article  Google Scholar 

  3. Gumus, E., Kilic, N., Sertbas, A., Ucan, O.N.: Evaluation of face recognition techniques using PCA, wavelets and SVM. Expert Syst. Appl. 37(9), 6404–6408 (2010)

    Article  Google Scholar 

  4. Li, S.Z., Jain, A.K.: Handbook of Face Recognition. Springer, London (2005). https://doi.org/10.1007/978-0-85729-932-1. ISBN 0-387-40595-X

    Book  MATH  Google Scholar 

  5. Chude-Olisah, C.C., Sulong, G., Chude-Okonkwo, U.A., Hashim, S.Z.: Illumination normalization for edge-based face recognition using the fusion of RGB normalization and gamma correction. In: IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 412–416 (2013)

    Google Scholar 

  6. Du, S., Ward, R.: Wavelet-based illumination normalization for face recognition. In: IEEE International Conference on Image Processing (ICIP), vol. 2, pp. 954–956 (2005)

    Google Scholar 

  7. Zong, W., Huang, G.B.: Face recognition based on extreme learning machine. Neurocomputing. 74(16), 2541–2551 (2011)

    Article  Google Scholar 

  8. Zong, W., Zhou, H., Huang, G.-B., Lin, Z.: Face recognition based on kernelized extreme learning machine. In: Kamel, M., Karray, F., Gueaieb, W., Khamis, A. (eds.) AIS 2011. LNCS (LNAI), vol. 6752, pp. 263–272. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21538-4_26

    Chapter  Google Scholar 

  9. Iosifidis, A., Tefas, A., Pitas, I.: Enhancing ELM-based facial image classification by exploiting multiple facial views. In: Procedia Computer Science: International Conference on Computational Science, vol. 51, pp. 2814–2821. Elsevier (2015)

    Google Scholar 

  10. Rujirakul, K., So-In, C.: Histogram equalized deep PCA with ELM classification for expressive face recognition. In: IEEE International Workshop on Advanced Image Technology (2018)

    Google Scholar 

  11. Wang, Y., Li, H., Guo, Y.: Face recognition based on ICA and SPSO-ELM. In: IEEE Information Technology, Networking, Electronic and Automation Control Conference, pp. 602–606 (2018)

    Google Scholar 

  12. Zhang, G.Y., Peng, S.Y., Li, H.M.: Combination of dual-tree complex wavelet and SVM for face recognition. In: Proceedings of International Conference on Machine Learning and Cybernetics, vol. 5, pp. 2815–2819 (2008)

    Google Scholar 

  13. Gan, J.Y., He, S.B.: Face recognition based on 2DLDA and support vector machine. In: Proceedings of International Conference on Wavelet Analysis and Pattern Recognition, pp. 211–214 (2009)

    Google Scholar 

  14. Zhao, L., Song, Y., Zhu, Y., Zhang, C., Zheng, Y.: Face recognition based on multiclass SVM. In: Proceedings of Chinese Control and Decision Conference, pp. 5871–5873 (2009)

    Google Scholar 

  15. Salhi, A.I., Kardouchi, M., Belacel, M.: Histograms of fuzzy oriented gradients for face recognition. In: IEEE International Conference on Computer Applications Technology (2013)

    Google Scholar 

  16. Wang, H., Zhang, D., Miao, Z.: Fusion of LDB and HOG for face recognition. In: IEEE 37th Chinese Control Conference, pp. 9192–9196 (2018)

    Google Scholar 

  17. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006). Elsevier

    Article  Google Scholar 

  18. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Real-time learning capability of neural networks. IEEE Trans. Neural Netw. 17(4), 863–878 (2006)

    Article  Google Scholar 

  19. Huang, G.B.: The MATLAB code for ELM (2004). http://www.ntu.edu.sg/home/egbhuang

  20. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  21. Lo, C., Chow, P.: A high-performance architecture for training Viola-Jones object detectors. In: IEEE International Conference on Field-Programmable Technology, pp. 174–181 (2012)

    Google Scholar 

  22. Mathworks MATLAB: Detection objects using the Viola-Jones algorithm. Mathworks MATLAB Documentation R2018b (2012). https://in.mathworks.com/help/vision/ref/vision.cascadeobjectdetector-system-object.html

  23. Dalal, N., Triggs, B.: Histogram of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)

    Google Scholar 

  24. Korkmaz, S.A., Akçiçek, A., Bínol H., Korkmaz, M.F.: Recognition of the stomach cancer images with probabilistic HOG feature vector histograms by using HOG features. In: IEEE International Symposium on Intelligent Systems and Informatics (SISY), pp. 339–342 (2017)

    Google Scholar 

  25. Mathworks MATLAB: Extract histogram of oriented gradients (HOG) features. Mathworks Matlab Documentation R2018b (2013). https://in.mathworks.com/help/vision/ref/extracthogfeatures.html?s_tid=doc_ta

  26. AT&T Laboratories Cambridge: The AT&T Dataset (formerly ‘The ORL Dataset of Faces’). http://www.cl.cam.ac.uk/Research/DTG/attarchive:pub/data/att_faces.zip

  27. YALE Face Dataset. http://cvc.cs.YALE.edu/cvc/projects/YALEfaces/YALEfaces.html

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Acknowledgments

The authors would like to thank University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University and Deen Dayal Upadhyaya College, University of Delhi for providing the necessary software and infrastructure support. The authors also acknowledge Faculty of ESTEM, University of Canberra for providing the necessary financial support.

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Authors and Affiliations

  1. University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, New Delhi, 110078, India

    Khushwant Sehra

  2. Deen Dayal Upadhyaya College, University of Delhi, New Delhi, 110078, India

    Ankit Rajpal & Anurag Mishra

  3. Faculty of Science and Technology, University of Canberra, Bruce, ACT, 1617, Australia

    Girija Chetty

Authors
  1. Khushwant Sehra
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  2. Ankit Rajpal
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  3. Anurag Mishra
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  4. Girija Chetty
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Corresponding author

Correspondence to Ankit Rajpal .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg , Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

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Sehra, K., Rajpal, A., Mishra, A., Chetty, G. (2019). HOG Based Facial Recognition Approach Using Viola Jones Algorithm and Extreme Learning Machine. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11623. Springer, Cham. https://doi.org/10.1007/978-3-030-24308-1_35

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  • DOI: https://doi.org/10.1007/978-3-030-24308-1_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24307-4

  • Online ISBN: 978-3-030-24308-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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