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Performance Evaluation of Different Halftone Kernels for Binary Face Recognition

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Intelligent Techniques and Applications in Science and Technology (ICIMSAT 2019)

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 12))

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Abstract

Face recognition is done gray or color images. Face recognition can be also done in binary images. Halftone images are binary images having gray shade information. These images are more informative as compared to binary images. Halftone images can be generated from different kernels. The quality of a halftone image depends on the kernel used to generate it. Halftone images can be used to generate human recognizable features for face recognition. The human recognizable features are fed to multiclass SVM classifiers for face recognition. The performance of using different human recognizable features generated from halftone images of different standard kernels is studied. It is found that some of kernels give higher recognition rate than the other. Also, the performance is also dependent on size of the window used for feature extraction. In most cases, the minimum size window 3 × 3 gives higher recognition rate in most of the cases. Of the standard kernels Frankie Siera-3, Stucki kernels give higher recognition rate and of the proposed kernels P2 kernel gives higher recognition rate than other proposed kernels.

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References

  1. Albiol, A., Monzo, D., Martin, A., Sastre, J.: Face recognition using HOG–EBGM. Pattern Recogn. Lett. 29(10), 1537–1543 (2008)

    Article  Google Scholar 

  2. Aly, M.: Face recognition using SIFT features. http://bayanbox.ir/view/4119301154021935385/Face-Recognition-using-SIFT-Features.pdf

  3. Bledsoe, W.W.: Man-machine facial recognition. Panoramic Research Inc., Palo Alto, California, Report PRI:22, August 1966

    Google Scholar 

  4. Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2, 121–167 (1998)

    Article  Google Scholar 

  5. Shu, C., Ding, X., Fang, C.: Histogram of the oriented gradient for face recognition. ieeexplore.ieee.org/iel5/5971803/6077972/06077989.pdf

  6. Deniz, O., Bueno, G., Salido, J., De la Torre, F.: Face recognition with HoG. Pattern Recogn. Lett. 32, 1598–1603 (2011)

    Article  Google Scholar 

  7. Du, G., Su, F., Cai, A.: Face recognition using SURF features. https://pdfs.semanticscholar.org/192e/439b19824d06bb21ad6bd63cc7a55772549f.pdf

  8. Forlov, I., Sardykhov, R.: The techniques for face recognition with SVM. In: Proceedings International Multiconference on Computer Science and Information Technology, Mragowo, Poland, 12–14 October 2009

    Google Scholar 

  9. Hanmandlu, M., Gupta, D., Vasikarla, S.: Face recognition using elastic bunch graph matching. ieeexplore.ieee.org/document/6749338/

  10. Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Trans. Neural Netw. 8(1), 98–113 (1997)

    Article  Google Scholar 

  11. Kanade, T.: Picture processing system by computer complex and recognition of human faces. Technical report, Department of Information Science, Koyoto University (1973)

    Google Scholar 

  12. Kelly, M.D.: Visual identification of people by computer. Technical report, Stanford University California, Department of Computer Science (1970)

    Google Scholar 

  13. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)

    Google Scholar 

  14. Križaj, J., Štruc, V., Pavešić, N.: Adaptation of SIFT features for robust face recognition. https://link.springer.com/chapter/10.1007/978-3-642–13772-3_40

  15. Phillips, P.J.: Support vector machines applied to face recognition. In: Advances in Neural Information Processing Systems, pp. 803–809 (1999)

    Google Scholar 

  16. Singh, Y.K.: Generalized error diffusion method for halftoning. In: Proceedings ICECCT 2015, vol. 3, pp. 1519–1524, March 2015

    Google Scholar 

  17. Singh, Y.K., Hruaia, V.: Halftone based face recognition using SVM. In: ICAICR 2019, Deventure Shimla Hills, 15–16 June 2019 (2019, Accepted)

    Google Scholar 

  18. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3, 71–86 (1991)

    Article  Google Scholar 

  19. Vanlalhruaia, Singh, Y.K., Singh, N.D.: Binary face image recognition using logistic regression and neural network. In: ICECDS 2017, SKR College of Engineering, Tamil Nadu, India, 1–2 August 2017

    Google Scholar 

  20. AT&T Face Database. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

Download references

Author information

Authors and Affiliations

  1. Centre for Development of Advanced Computing, Ground Floor, IIPC Building, NIT Silchar Campus, Silchar, Assam, India

    Yumnam Kirani Singh

  2. National Institute of Electronics and Information Technology, Aizawl, Mizoram, India

    Vanlalhruaia

Authors
  1. Yumnam Kirani Singh
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  2. Vanlalhruaia
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Corresponding author

Correspondence to Yumnam Kirani Singh .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Siliguri Institute of Technology, Sukna, West Bengal, India

    Subhojit Dawn

  2. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Psychology and IIASS, Università della Campania “Luigi Vanvitelli”, Caserta, Caserta, Italy

    Anna Esposito

  4. Mizoram University, Aizawl, Mizoram, India

    Sadhan Gope

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Singh, Y.K., Vanlalhruaia (2020). Performance Evaluation of Different Halftone Kernels for Binary Face Recognition. In: Dawn, S., Balas, V., Esposito, A., Gope, S. (eds) Intelligent Techniques and Applications in Science and Technology. ICIMSAT 2019. Learning and Analytics in Intelligent Systems, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-42363-6_33

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