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@article{158680,
        author = {Vaishnavi M. Shinde and Dhanashri B. Shinde and Pravin B. Shinde and Aditya V. Wandhekar and Pallavi S. Kohakade},
        title = {Virtual ATM through Fingerprint and Face recognition using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {10},
        pages = {381-387},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=158680},
        abstract = {AbstractFingerprints and facial features of the individual are being used in biometric authentication techniques, which are increasingly extensively used across significant implementations. Despite the fact that there multiple facial recognition systems accessible. A greater number of research should unearth factors that improve efficiency and accuracy. Facial as well as fingerprint identification play an important part in the identifying process since they do not need human assistance, unlike some other biometrics methods. This not only proves the huge potential to create far greater protection for such Virtual ATM transactions, but also explains the reasoning why biometric identification systems have been attracting so much attention. Therefore, for this purpose an effective framework for biometric authentication on Virtual ATMs through the use of biometric features, such as Facial and Fingerprint have been proposed. The presented framework utilizes Live Streaming and Region of Interest along with Channel boosted Convolutional Neural Networks and OTP authentication has been implemented. The framework has been measured using lengthy experimentations to achieve quite reassuring outcomes.

},
        keywords = {Virtual ATM, Biometric recognition. Face recognition, Fingerprint Recognition, Channel Boosted Convolutional Neural Networks.},
        month = {},
        }