An online voting system using face recognition is adigital platform designed to enhance the security and accuracy of the voting process. The system utilizes facial recognition technology to verify the identity of voters, ensuring that only eligible voters can participate in the election. This system eliminates the need for physical polling stations, reducing costs and increasing accessibility for voters. The abstract of this system would detail its features, including its ability to authenticate voter identities, securely store votes,and prevent fraud. It would also discuss the benefits of using such a system, such as increased voter turnout and improvedtransparency in the electoral process. Object Detection using Haar featurebased cascade classifiers is an effective object detection method. Local Binary Pattern (LBP) is a simple yetvery efficient texture operator which labels the pixels of an image. Then the server checks for the data from the databaseand compares that data which is already existing in database.If the data matches with the already stored information, the person is allowed to poll the vote. If not, a message is displayed on the screen and therefore the person is not allowed to poll the vote. Overall, an online voting system using face recognition technology has the potential to revolutionize the way we conduct elections, making the process more efficient, secure, and accessible for all.

The GaussianFace algorithm developed in 2014 by researchers at The Chinese University of Hong Kong achieved facial identification scores of 98.52% compared with the 97.53% achieved by humans. An excellent rating, despite weaknesses regarding memory capacity required and calculation times.

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In June 2015, Google went one better with FaceNet. On the widely used Labeled Faces in the Wild (LFW) dataset, FaceNet achieved a new record accuracy of 99.63% (0.9963 0.0009).  

Using an artificial neural network and a new algorithm, the company from Mountain View has managed to link a face to its owner with almost perfect results.

In May 2018, Ars Technica reported that Amazon is already actively promoting its cloud-based face recognition service named, Rekognition, to law enforcement agencies. The solution could recognize as many as 100 people in a single image and perform face matches against databases containing tens of millions of faces.

At the end of May 2018, the US Homeland Security Science and Technology Directorate published the results of sponsored tests at the Maryland Test Facility (MdTF). These real-life tests measured the performance of 12 face recognition systems in a corridor measuring 2 m by 2.5 m.

Thales' solution utilizing Facial recognition software (LFIS) achieved excellent results with a face acquisition rate of 99.44% in less than 5 seconds (against an average of 68%), a Vendor True Identification Rate of 98% in less than 5 seconds compared with an average 66%. It also achieved an error rate of 1% compared with an average of 32%.

More on performance benchmarks: The NIST (National Institute of Standards and Technology) report, published in November 2018, details recognition accuracy for 127 algorithms and associates performance with participant names.

In NIST's reports (August 2020 and March 2021) entitled "Face recognition accuracy with face masks using post-COVID-19 algorithms", we see how algorithms are increasing their performance in less than a year.

Facial Emotion Recognition (from real-time or static images)is the process of mapping facial expressions to identify emotions such as disgust, joy, anger, surprise, fear, or sadness - or compound emotion such as sadness, anger - on a human face with image processing software.

It's a central component of the latest-generation algorithms developed by Thales and other key players. It holds the secret to face detection, face tracking, face match, and real-time translation of conversations. 

According to Forbes, digital account opening (DAO) was the most popular technology in banking for the third consecutive year. Nearly 80% of financial institutions add new DAO systems or enhance existing ones in 2020 and 2021.

While the United States currently offers the largest market for face recognition opportunities, the Asia-Pacific region sees the fastest growth in the sector. China and India lead the field.

The Superior Electoral Court (Tribunal Superior Eleitoral) is involved in Brazil's nationwide biometric data collection project. The aim is to create a biometric database and unique I.D. cards, recording the information of 140 million citizens.

The California Consumer Privacy Act (CCPA), passed in June 2018 and effective as of 1 January 2020, will severely impact privacy rights and consumer protection for residents of California and the whole nation.

A New York State law called the Stop Hacks and Improve Electronic Data Security (SHIELD) became effective on 21 March 2020. It requires implementing a cybersecurity program and protective measures for N.Y. State residents. 

Privacy and civil rights concerns have escalated in the country as face recognition gains traction as a law enforcement tool, and on 6 May 2019, San Francisco voted to ban facial recognition.

Stay tuned for the outcome of all these discussions as the U.S. Congress is getting pressure from activists to ban the technology and from providers) to regulate.

The E.U. Commission plans to act on the indiscriminate use of facial identifier technology. The European Commission president Ursula von der Leyen wants a coordinated approach to artificial intelligence's human and ethical implications. She has pledged to publish an A.I. legislation blueprint very soon.

The final version of the European Commission whitepaper is available online. The European Commission presented tough draft rules in April 2021. But according to Reuters, it could take years before the regulations come into force.

In India, thanks to the Puttaswamy judgment delivered on 27 August 2017, the Supreme Court has enshrined the right to privacy in the country's constitution. This decision has rebalanced the relationship between citizens and the state and posed a new challenge to expanding the Aadhaar project.

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In this paper a new authentication technique is discussed i-e; facial recognition verification for online voting system. It aims to develop a computerized voting system to make the election process more secure and user friendly. The electorate want to visit distinct locations like polling cubicles and stand in an extended queue to cast their vote, because of such reasons most of the people skip their chance of voting. The voter who isn\'t eligible also can forged its vote via way of means of faux way which can also additionally cause many problems. That\'s why in this project we have proposed a system or way for voting which is very effective or useful in voting. This system can also save money of the government which is spent in the election process. Overall this project is being developed to help staff of election commission of India and also reduce the human efforts.

The basic methodology as applied to online voting systems would involve giving voters realistic voting tasks to accomplish using a variety of ballot design. Voting task performance is measured using variables such as accuracy, time and workload. The voting server collects the vote and filters out duplicate or invalid votes. Each voter can then check his vote online to ensure that his vote has been counted.

Election plays an important role in such a huge democratic country like India where the leader is elected by residents.Elections preserve a truthful state functioning, as they provide people the choice to select their personal government. So the election ought to be an unfastened and truthful process. Every citizen of a democratic country has a right of voting with his/her own choice. One of the fundamental issues in the conventional democratic framework is that it expends bunches of labor and resources.Also some humans can be worried about illegal publications of movement at some point of this manner of election or its preparation. There are some disadvantages of the conventional election voting process which is being used in our country such as machine stops working, chances of brutality, time consuming, resource consuming, spot arranged etc. Many people couldn't vote because the voter has to reach the poll booths to vote or some people like those who are living far away from their original birth place where they are allowed to vote. So to get rid of their drawbacks, a new System is introduced i.e. Online Voting System, which provides accuracy, security, flexibility, mobility etc. An online voting System in a web based application to use in the election process. Initially ballot paper technique was used in the election process. Then the Electronic Voting Machine comes, these are easy to store the data and easily manageable. These are more secure than the ballot paper and less time consuming.Now, we proposed a system with biometric authentication to make the voting process more secure and reduce the time taken in the voting process. By the use of this, the electorate can solidify their vote for his or her preferred candidate through the use of their system. We use Face detection and Recognition Technology for authentication of citizens that he/she is the proper consumer or not. We provide many modules in which admin can login withinside the tool and show the numerous operations.Also users can login in the system and use their right to vote. When the Voter uses the system, the system will capture his/her image using a web camera & try to match with the image stored in the database. If both images are the same then the voter can cast his/her vote. Most higher learning establishments in Kenya conduct elections routinely to elect an understudy leadership to choose them. They proposed the process of an online system, which includes systems like enlistment of voters, vote casting, vote checking, and pronouncing results which would establish a decent answer for a substitute of framework that is in the institutes in Kenya. 152ee80cbc

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