Smart Voting System through Facial Recognition

Authors

  • Nilam Choudhary CSE, JECRC, Jaipur, India
  • Shikhar Agarwal CSE, JECRC, Jaipur, India
  • Geerija Lavania CSE, JECRC, Jaipur, India

Keywords:

Smart Voting, Facial Recognition, EigenFace, FisherFace, SURF

Abstract

Facial recognition is a category of biometric software which works by matching the facial features. We will be studying the implementation of various algorithms in the field of secure voting methodology. There are three levels of verification which were used for the voters in our proposed system. The first is UID verification, second is for the voter card number, and the third level of verification includes the use of various algorithms for facial recognition. In this paper, we will provide a comparative study between these algorithms, namely: Eigenface, FisherFace & SURF.

 

References

A. Baumberg, “Reliable feature matching across widely separated views”, in CVPR, 2000

F. Song, D. Zhang, J. Wang, H. Liu, and Q. Tao, "A parameterized direct LDA and its application to face recognition," Neurocomputing, Vol.71, 2007.

H. Bay, T. Tuytelaars, L. Van Gool, “SURF: speeded up robust features”, in ECCV, 2006.

KashifHussainMemon, Dileep Kumar and Syed Muhammad Usman, “Next Generation A Secure EVoting System Based On Biometric Fingerprint Method”, 2011.

P. Belhumeur, J. Hespanha, and D. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):711--720, 1997.

P. Viola and M. J. Jones, "Robust real-time face detection," International Journal of Computer Vision, Vol. 57, pp. 137-154, 2004.

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Published

2019-04-30

How to Cite

[1]
N. Choudhary, S. Agarwal, and G. Lavania, “Smart Voting System through Facial Recognition”, Int. J. Sci. Res. Comp. Sci. Eng., vol. 7, no. 2, pp. 7–10, Apr. 2019.

Issue

Section

Research Article

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