Enhanced Security for ATM Transactions using Facial Verification
|International Journal of Electronics and Communication Engineering|
|© 2016 by SSRG - IJECE Journal|
|Volume 3 Issue 3|
|Year of Publication : 2016|
|Authors : P.Bala Saiteja, K.Vasavi, M.A.Sathveek Prasad, K.Ramakrishna and V.V.K.D.V.Prasad|
How to Cite?
P.Bala Saiteja, K.Vasavi, M.A.Sathveek Prasad, K.Ramakrishna and V.V.K.D.V.Prasad, "Enhanced Security for ATM Transactions using Facial Verification," SSRG International Journal of Electronics and Communication Engineering, vol. 3, no. 3, pp. 5-7, 2016. Crossref, https://doi.org/10.14445/23488549/IJECE-V3I3P102
There is an urgent need for improving security in banking region. With the birth of the Automatic Teller Machines, banking became a lot easier though with its own troubles of insecurity. Due to tremendous increase in the number of criminals and their activities, the ATM has become insecure. ATM systems today use no more than an access card and PIN for identity verification. The recent progress in biometric identification techniques, including finger printing, retina scanning, and facial recognition has made a great efforts to rescue the unsafe situation at the ATM. This research looked into the development of a system that integrates facial recognition technology into the identity verification process used in ATMs. An ATM model that is more reliable in providing security by using facial recognition software is proposed .The development of such a system would serve to protect consumers and financial institutions alike from intruders and identity thieves .In this an automatic teller machine security model is proposed that would combine a physical access card, a PIN, and electronic facial recognition that will go as far as withholding the fraudster’s card. If this technology becomes widely used, faces would be protected as well as PINs. However, it obvious that man’s biometric features cannot be replicated, this proposal will go a long way to solve the problem of Account safety making it possible for the actual account owner alone have access to his accounts. The combined biometric features approach is to serve the purpose both the identification and authentication that card and PIN do.
ATM Security, Face Verification, PIN, Access card.
 Juhi Malhotra and Netra Raina, ―Biometric Face Recognition and Issues, Second IEEE International Conference on Computing for Sustainable Global Development, pp. 1239- 124, March 2015.
 Thiago H.H. Zavaschi, Alceu S. Britto Jr. Luiz E.S. Oliveira, Alessandro L. Koerich, ―Fusion of feature sets and classifiers for facial expression recognition, ELSEVIER International journal on Expert Systems with Applications, Vol. 40, Issue 2, pp. 646– 655, February 2013.
 Soma Biswas, Gaurav Aggarwal, Patrick J. Flynn, and Kevin W. Bowyer, ―Pose-Robust Recognition of Low-Resolution Face Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 12, pp. 3037- 3049, December 2013.
 Ramzi Abiantun, Utsav Prabhu, and Marios Savvides, ―Sparse Feature Extraction for Pose-Tolerant Face Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, No. 10, pp. 2061 – 2073, October 2014.
 Jiwen Lu, Venice Erin Liong, Gang Wang, and Pierre Moulin, ―Joint Feature Learning for Face Recognition, IEEE Transactions on Information Forensics and Security, Vol. 10, No. 7, pp. 1371 -1383, July 2015.
 Bing-Kun Bao, Guangcan Liu, Changsheng Xu and Shuicheng YanKai, ―Inductive Robust Principal Component Analysis, IEEE Transactions on Image Processing, Vol. 21, No. 8, pp. 3794- 3800, August 2012.
 Huu-Tuan Nguyen and Alice Caplier, ―Local Patterns of Gradients for Face Recognition, IEEE Transactions on Information Forensics and Security, Vol. 10, No. 8, pp. 1739 – 1751, August 2015.
 Changxing Ding, Chang Xu, and Dacheng Tao, ―Multi-task PoseInvariant Face Recognition, IEEE Transactions on Image Processing, Vol. 24, No. 3, pp. 980 – 993, March 2015.
 Mehran Kafai, Kave Eshghi, and Bir Bhanu ―Discrete Cosine Transform Locality-Sensitive Hashes for Face Retrieval, IEEE Transactions on Multimedia, Vol. 16, No. 4, pp. 1090 -1103, June 2014.
 Loris Nanni, Alessandra Lumini, and Sheryl Brahnam, ―Survey on LBP based texture descriptors for image classification, ELSEVIER International Journal on Expert Systems with Applications, Vol. 39, Issue 3, pp. 3634-3641, February 2012.
 Baochang zhang, Lei zhang, David zhang and Linlin shen, ―Directional Binary Code with Application to PolyU Near- Infrared Face Database, ELSEVIER Pattern Recognition Letters, Vol. 31, Issue 14, pp. 2337- 2344, October 2010.
 Jagadeesh H S, Suresh Babu K and Raja K B, ―DBC based Face Recognition using DWT, An International Journal in Signal & Image Processing, Vol.3, No.2, pp. 115 – 130, April 2012.
 Giovanni Betta, Domenico Capriglione, Mariella Corvino, Consolatina Liguori, and Alfredo Paolillo, ―Face Based Recognition Algorithms: A First StepToward a Metrological Characterization, IEEE Transactions on Instrumentation and Measurement, Vol. 62, No. 5, pp. 1008 -1016, May 2013.