Fingerprint and Face Authentication Portable Digital Electronic Voting Machine

The paper presents the Electronic Voting Machine (EVM) device to facilitate digital elections with the use of Raspberry Pi as its brain. It is developed to reduce paper wastage and prevent fraud and ghost voting in contrast to the Paper Ballot System. The EVM is designed to authenticate the voter through fingerprint or face authentication. The face authentication method is incorporated with an anti-spoofing technique. Local Binary Pattern Histograms (LBPH) and Haar-Cascade Classifier models were used to detect and recognize faces and eye-blink counters for anti-spoofing. Both authentications were combined and incorporated into the voting graphic user interface (GUI), programmed through Python and its libraries to create the EVM, wherein the voter must authenticate through fingerprint or face recognition if they failed the prior method. The accuracy tests resulted in an overall 80% and 95.56% accuracy for fingerprint and face authentication, respectively. The anti-spoofing technique resulted in an overall accuracy of 100%. The mock elections of 20 voters conducted to test the functionality of the EVM resulted in an 80% fingerprint authentication success rate, while 100% of the remaining 20% passed the face authentication. Those who failed the fingerprint authentication proceeded to face authentication, resulting in an overall 100% success rate in the authentication of all voters. The database results matched the receipt of the voters, demonstrating the device’s overall efficiency of 100%. In conclusion, the paper proposes an efficient and secure EVM that ensures fair and transparent digital elections.