Design and Implementation of an Image Based System to Enhance the Security of ATM
In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined.
[1] S. Sruthy. "Literature Survey on Automated Person Identification Techniques." (2013).
[2] M. T. Harandi, M. Nili Ahmadabadi, and B. N. Araabi. "Optimal local basis: A reinforcement learning approach for face recognition." International Journal of Computer Vision 81, no. 2 (2009): 191-204.
[3] M. Sharif, A. Khalid, M. Raza, and S. Moshin. "Face Recognition using Gabor Filters." Journal of Applied Computer Science 5 (2011).
[4] H, Lee, C. Wu, and T. Lin. "Facial Expression Recognition Using Image Processing Techniques and Neural Networks." In Advances in Intelligent Systems and Applications-Volume 2, pp. 259-267. Springer Berlin Heidelberg, 2013.
[5] J. S. Leibold, A. Riehl, J. Hettinger, M.Durben, J. Hess, and P. Angel. "Keratinocyte-Specific Deletion of the Receptor RAGE Modulates the Kinetics of Skin Inflammation In Vivo." Journal of Investigative Dermatology (2013).
[6] G. A. Papakostas, V. G. Kaburlasos, and T.Pachidis. "Thermal infrared face recognition based on lattice computing (LC) techniques." In Fuzzy Systems (FUZZ), 2013 IEEE International Conference on, pp. 1-6. IEEE, 2013.
[7] J. Chang. "Facial surveillance system and method." U.S. Patent Application 10/429,720, filed May 6, 2003.
[8] P. Viola and M. J. Jones. "Robust real-time face detection." International journal of computer vision 57, no. 2 (2004): 137-154.
[9] Y, Freund, and Robert E. Schapire. "A desicion-theoretic generalization of on-line learning and an application to boosting." In Computational learning theory, pp. 23-37. Springer Berlin Heidelberg, 1995.
[10] S. N. Tayarani Bathaie, "Design and implementation of an image receiving system with the ability of face detection to be served in ATM system", Msc Thesis, Department of Internet Technology and computer engineering, Amirkabir University of Technology, 90 pages.
[11] W. B. Pennebaker. JPEG: Still image data compression standard. Springer, 1992, pp. 78_79.
[12] Http://en.wikipedia.org/wiki/Triple_Dess.
[1] S. Sruthy. "Literature Survey on Automated Person Identification Techniques." (2013).
[2] M. T. Harandi, M. Nili Ahmadabadi, and B. N. Araabi. "Optimal local basis: A reinforcement learning approach for face recognition." International Journal of Computer Vision 81, no. 2 (2009): 191-204.
[3] M. Sharif, A. Khalid, M. Raza, and S. Moshin. "Face Recognition using Gabor Filters." Journal of Applied Computer Science 5 (2011).
[4] H, Lee, C. Wu, and T. Lin. "Facial Expression Recognition Using Image Processing Techniques and Neural Networks." In Advances in Intelligent Systems and Applications-Volume 2, pp. 259-267. Springer Berlin Heidelberg, 2013.
[5] J. S. Leibold, A. Riehl, J. Hettinger, M.Durben, J. Hess, and P. Angel. "Keratinocyte-Specific Deletion of the Receptor RAGE Modulates the Kinetics of Skin Inflammation In Vivo." Journal of Investigative Dermatology (2013).
[6] G. A. Papakostas, V. G. Kaburlasos, and T.Pachidis. "Thermal infrared face recognition based on lattice computing (LC) techniques." In Fuzzy Systems (FUZZ), 2013 IEEE International Conference on, pp. 1-6. IEEE, 2013.
[7] J. Chang. "Facial surveillance system and method." U.S. Patent Application 10/429,720, filed May 6, 2003.
[8] P. Viola and M. J. Jones. "Robust real-time face detection." International journal of computer vision 57, no. 2 (2004): 137-154.
[9] Y, Freund, and Robert E. Schapire. "A desicion-theoretic generalization of on-line learning and an application to boosting." In Computational learning theory, pp. 23-37. Springer Berlin Heidelberg, 1995.
[10] S. N. Tayarani Bathaie, "Design and implementation of an image receiving system with the ability of face detection to be served in ATM system", Msc Thesis, Department of Internet Technology and computer engineering, Amirkabir University of Technology, 90 pages.
[11] W. B. Pennebaker. JPEG: Still image data compression standard. Springer, 1992, pp. 78_79.
[12] Http://en.wikipedia.org/wiki/Triple_Dess.
@article{"International Journal of Information, Control and Computer Sciences:65965", author = "Seyed Nima Tayarani Bathaie", title = "Design and Implementation of an Image Based System to Enhance the Security of ATM", abstract = "In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined.
", keywords = "Face detection algorithm, Haar features, Security of ATM.", volume = "7", number = "12", pages = "1598-4", }
