Multi-biometric cryptosystem using graph for secure cloud authentication

2020 ◽  
Vol 38 (5) ◽  
pp. 6437-6444
Author(s):  
R. Ashoka Rajan ◽  
P. Kumaran
Keyword(s):  
Biometric Cryptosystem

scholarly journals Model of the "biometry-code" converter based on artificial neural networks for analysis of facial thermograms

2021 ◽  
pp. 154-165
Author(s):  
Pavel Lozhnikov ◽  
◽  
Samal Zhumazhanova ◽  
Keyword(s):  
Social Engineering ◽  
New Approach ◽  
Thermal Images ◽  
Code Converter ◽  
Biometric Security ◽  
Psychophysiological State ◽  
Biometric Parameter ◽  
The Subject ◽  
Biometric Cryptosystem ◽  
Authorized Access

Existing asymmetric encryption algorithms involve the storage of a secret private key, authorized access to which, as a rule, is carried out upon presentation of a password. Passwords are vulnerable to social engineering and human factors. Combining biometric security techniques with cryptography is seen as a possible solution to this problem, but any biometric cryptosystem should be able to overcome the small differences that exist between two different implementations of the same biometric parameter. This is especially true for dynamic biometrics, when differences can be caused by a change in the psychophysiological state of the subject. The solution to the problems is the use of a system based on the "biometrics-code" converter, which is configured to issue a user key after presentation of his/her biometric image. In this case, the key is generated in advance in accordance with accepted standards without the use of biometric images. The work presents results on using thermal images of a user for reliable biometric authentication based on a neural network "biometrics-code" converter. Thermal images have recently been used as a new approach in biometric identification systems and are a special type of biometric images that allow us to solve the problem of both the authentication of the subject and the identification of his psychophysiological state. The advantages of thermal imaging are that this technology is now becoming available and mobile, allowing the user to be identified and authenticated in a non-contact and continuous manner. In this paper, an experiment was conducted to verify the images of thermograms of 84 subjects and the following indicators of erroneous decisions were obtained: EER = 0.85 % for users in the "normal"state.

Multimodal biometric cryptosystem for human authentication using fingerprint and ear

2019 ◽  
Vol 79 (1-2) ◽  
pp. 659-673 ◽  
Author(s):  
Padira S. V. V. N. Chanukya ◽  
T. K. Thivakaran
Keyword(s):  
Biometric Cryptosystem

Symmetric keyring encryption scheme for biometric cryptosystem

2019 ◽  
Vol 502 ◽  
pp. 492-509 ◽  
Author(s):  
Yen-Lung Lai ◽  
Jung Yeon Hwang ◽  
Zhe Jin ◽  
Soohyong Kim ◽  
Sangrae Cho ◽  
...  
Keyword(s):  
Encryption Scheme ◽  
Biometric Cryptosystem
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