ATM Security Using Image Processing in Machine Learning

Abstract

The real-time face detection and recognition has been made possible by using the method of Viola jones, Analysis work. The software first taking images of all persons and stores the information into database. Proposed work deals with automated system to detect person. The methodology comprised of three phases, first face Detection from image, second get all detail of face for the purpose of feature extraction. The most useful and unique features of the camera image are extracted in the feature extraction phase. Find out all facial details are visible. This feature vector forms an efficient representation of the face. In third phase and grab our feature extraction has been created to find the person how osculated face. 

Country : India

1 Jalinder Ekatpure2 Dheeraj Nair3 Madhav Deshpande4 Sandip Sagare5 Pankaj Jadhav

  1. Assistant Professor, B.E., Computer engineering, SPVP, S.B.Patil College of Engineering, Pune, Maharashtra, India
  2. Student, B.E., Computer engineering, SPVP, S.B.Patil College of Engineering, Pune, Maharashtra, India
  3. Student, B.E., Computer engineering, SPVP, S.B.Patil College of Engineering, Pune, Maharashtra, India
  4. Student, B.E., Computer engineering, SPVP, S.B.Patil College of Engineering, Pune, Maharashtra, India
  5. Student, B.E., Computer engineering, SPVP, S.B.Patil College of Engineering, Pune, Maharashtra, India

IRJIET, Volume 5, Issue 6, June 2021 pp. 29-31

doi.org/10.47001/IRJIET/2021.506006

References

  1. X. Wei, C.-T. Li, Z. Lei, D. Yi, and S. Li, “Dynamic Image-to- Class Warping for Occluded Face Recognition,” IEEE Transactions on Information Forensics and Security, vol. 9, no. 12, pp. 2035–2050, Dec 2014.
  2. P. J. Phillips, J. R. Beveridge, B. A. Draper, G. Givens, A. J. O’Toole, D. S. Bolme, J. Dunlop, Y. M. Lui, H. Sahibzada, and S. Weimer, “An introduction to the good, the bad, the ugly face recognition challenge problem,” in 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG). IEEE, 2011, pp. 346–353.
  3. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deepface: Closing the gap to human-level performance in face verification,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014, pp. 1701–1708. 4 .J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210–227, 2009.
  4. A.Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma, “Toward a practical face recognition system: Robust alignment and illumination by sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 2, pp. 372–386, 2012.
  5. Bayly, M., Regan, M., Hosking, S.: ‘Intelligent transport systems and motorcycle safety’ (Monash University, Accident Research Centre, 2006), p. 260.
  6. Bianco, A., Trani, F., Santoro, G., Angelillo, I.F.: ‘Adolescents’ attitudes and behaviour towards motorcycle helmet use in Italy’, Eur. J. Pediatr., 2005, 164, (4), pp. 207– 211.