TIR/VIS Correlation for Liveness Detection in Face RecognitionDate:2021-09-17
Biometrics in banking security: a case study
An electronic voting (e-voting) system is a voting system in which the election data is recorded, stored and processed primarily as digital information. There are two types of e-voting: On-Line and Offline. On-line, e.g. via Internet, and offline, by using a voting machine or an electronic polling booth. Authentication of Voters, Security of voting process, Securing voted data are the main challenge of e-voting. This is the reason why designing a secure e-voting system is very important. In many proposals, the security of the system relies mainly on the black box voting machine. But security of data, privacy of the voters and the accuracy of the vote are also main aspects that have to be taken into consideration while building secure e-voting system. In this project the authenticating voters and polling data security aspects for e-voting systems was discussed. It ensures that vote casting cannot be altered by unauthorized person. The voter authentication in online e-voting process can be done by formal registration through administrators and by entering One time password. In Offline e-voting process authentication can be done using Iris recognization, finger vein sensing which enables the electronic ballot reset for allowing voters to cast their votes. Also the voted data and voters details can be sent to the nearby Database Administration unit in a timely manner using GSM System with cryptography technique.
A spoofing attack occurs as a person tries to masquerade as a valid user by presenting the fake biometric trait to the sensor. Liveness detection, or anti-spoofing, uses particular measures to reduce the vulnerability of biometric systems to the threats of spoofing attack. This paper presents an approach for real-time eye-blink detection for face liveness detection. First, the face and eye region are detection by using Viola and Jones cascaded AdaBoost algorithm. Then, the extracted image is binarized using simple addition operations to reduce the computational cost. Finally, a discrete measure is used to detect eye-blinks. Experiments indicate correct detection rate is about 88%, which indicates of the method's simplicity and effectiveness.
|0||Digital Identity Verification||Default|