Biometric data card and authentication methodDate:2021-09-17
ATM & BIOMETRICS: A SOCIO-TECHNICAL BUSINESS MODEL
Fingerprint Detection and recognition is the most Challenging and widely used Biometric technologies. Today's modern world, apparently it is used in many real applications. The real images of human identification characteristics are spoofed by Putty, Play-doh, Fingerprint mold, etc. Here we obtain a Probabilistic Neural Networks (PNN) used to oversee training set to develop probability density functions intense a pattern layer to fingerprint liveliness detection. This is a model based the core concept and based on multivariate probability estimation. Yield state-of-the-art results for architecture or hyper parameter selection is not needed for pre-trained PNNs., Not only for extreme architectures but also for requiring ones used by Dataset Augmentation is to improve the performance. More advantaged accuracy on very small training sets using these large pre - exercise networks. Our best model achieves an overall rate of 95.1% of correctly exercised classified samples.
The article discusses that how banks and independent ATM operators can use biometrics-based security instead of passwords to provide better security to costumers. Topics discussed include how adoption of multi-factor authentication methods, such as the use of biometrics alongside one-time pass codes can help financial services providers to increase the security, customer retention becoming difficult in digital banking era, and need to redefine banking customer services.
|0||Digital Identity Verification||Default|