| Title |
Fusion Biometric Deep Features Blended In Its Authentication |
| ID_Doc |
27454 |
| Authors |
Rao M.G.; Pawar S.; Priyanka H.; Reddy K.H.K.; Divakarala U. |
| Year |
2022 |
| Published |
2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022 |
| DOI |
http://dx.doi.org/10.1109/ICATIECE56365.2022.10047789 |
| Abstract |
Information security is a key factor in a world where technology is developing quickly in terms of data and data interpretations. The most important objective is to have a secure system. Biometrics, OTP generation, iris detection, and other security-related measures are done to secure the system. One of the more established security technologies is the biometric system. In the case of getting the security from the biometrics fails if it depends on only one biometric feature. Nowadays combinations of biometric features are developed to secure the system. The proposed system can have type 1, type 2, type 3 and type 4 security to access the ITS (Intelligent Transport System). The type1 has only one biometric feature. Type 2, 3 and 4 have a blending of 2,3and 4 biometric features. The proposed system considers biometric features such as periocular, iris, fingerprint and voice modulation. We applied the different deep-learning models to get the desired result © 2022 IEEE. |
| Author Keywords |
Authentication; Biometrics; CNN; Deep Learning; DenseNet; Fingerprint; Periocular; PRC; Pre-processing; Resnet50; Smart City; VGG16; Voice |
