Voting Booth Helper System Using Machine Learning

Elections are the fundamental defining characteristics of any democracy that is being governed by the people, where in people express their choices or articulate opinions through voting. The existing voting system uses EVM system at polling booths for voting and its main drawback is the manual validation of the voter. In the polling booths, the voting process is organized by few organizers having a count from 5 to 10 or even above. These people are assigned to perform certain tasks, one of such tasks is to validate the voter. With the raising population this consumes a lot of time, which in turn increases the man power and the human error. This project aims to provide an efficient solution to overcome the drawbacks of the existing voting system. We have developed a module using face recognition algorithm, to validate the voter accurately and efficiently within no time. It even reduces the man power, as it alone, performs all the tasks performed by the several organizers at the voting booths. The algorithm made use of, is the Multi-Task Cascaded Convolutional Neural Networks which is known for its accuracy and speed. The reduction of man power helps to control the rapid increase of covid cases, which is the most prevailing problem and helps the voters to vote with ease.