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Automated Voters Verification System Through Facial Recognition

Authors: V. Vibavan; S. Gayathri; K. Saranya;

Automated Voters Verification System Through Facial Recognition

Abstract

The automated voter verification system using face recognition aims to replace the manual verification of citizens during electoral polling. This system was designed with the objective to automatically identify each individual who votes, by capturing images and comparing the image with stored database image files. Extraction methods viz. PCA (Principal Component Analysis) –Thus we create a feature set for each of the images provided in the database. During real time, the images of human face may be extracted from a USB camera. The system will generate an internal list of the voters and non-voters during election. Better accuracy is attained in results and the system takes into account the changes that occurs in the face over the period of time. This system will reduce human efforts and also time required to verify the voters individually and also to eradicate the fraudulent voting. This paper will first audit the related works in the field of participation administration and face acknowledgment.

Keywords

Skin Classification, Image Acquisition, PCA, Eigenfaces, Open CV, Face Detection & Face Recognition., Skin Classification, Image Acquisition, PCA, Eigenfaces, Open CV, Face Detection & Face Recognition.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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