Smart Voting System Using Face Recognition Technology and AI-Driven Database Management for Secure Elections

The Smart Voting System using Facial Recognition is designed to improve the security, accuracy, and efficiency of modern electoral systems. Traditional voting methods are vulnerable to issues such as voter impersonation, fraud, and manual verification errors. To overcome these challenges, this system integrates facial recognition technology with machine learning techniques. The proposed system utilizes OpenCV for real-time image processing and Histogram of Oriented Gradients (HOG) for extracting distinctive facial features. These features are then classified using a Support Vector Machine (SVM) algorithm to verify the identity of voters. During the voting process, the voter’s face is captured using a webcam, pre-processed, and analyzed to extract HOG descriptors. The extracted features are compared with a pre-registered voter database using the trained SVM classifier. If the facial features match the stored records, the voter is authenticated and allowed to cast the vote. This approach reduces the chances of voter impersonation and improves the overall transparency of the voting process. The system is designed to be cost-effective, scalable, and suitable for real-time deployment in polling environments, making it a reliable alternative to traditional voting systems.