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@article{196888,
        author = {S Avinash and Patnam Suresh and Shaik Mahaboob basha and P Sudha},
        title = {A Real Time Online Voting System Using Facial Recognition and KNN Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4698-4705},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196888},
        abstract = {The Smart Online Voting System is designed to provide a secure, reliable, and transparent voting process using facial recognition technology. This system authenticates voters through face recognition based on the K-Nearest Neighbors (KNN) machine learning algorithm, ensuring the principle of one person, one vote. The project is developed using Python, OpenCV, and Scikit-learn, where OpenCV is used for face detection and image processing, and Scikit-learn is used for training and classification of facial data. Voter face data is collected and stored to generate a training dataset, enabling accurate voter verification during elections. Once authenticated, voters can cast their vote securely. The system also provides real-time election statistics, including pie charts, bar graphs, and vote percentage displays, allowing transparent monitoring of voting results. This approach reduces voting fraud, minimizes human intervention, and enhances trust in digital voting systems. To a proactive, intelligence-driven stance.},
        keywords = {Voting System, Face Recognition, K-Nearest Neighbors (KNN), Biometric Authentication, Online Voting, Machine Learning, Image Processing, Voter Authentication, Security, Fraud Prevention, Digital Voting, Facial Feature Extraction, Real-Time Recognition, E-Governance, Data Security, Liveness Detection, Pattern Recognition, Artificial Intelligence},
        month = {April},
        }