Secure Online Voting System-Based on Facial Recognition by using Deep Learning

Corporate elections, in general, suffer from limitations of traditional in-person and paper-based voting security vulnerabilities, inefficiencies, and logistical constraints. To counter these limitations, this paper recommends a secure online voting system specially designed for the corporate environment with the integration of advanced technologies, such as computer vision, deep learning, and OTP-based authentication, to make the voter verification process robust and tamper proof. With the use of distinct employee identification, for instance, employee ID, or biometric credentials, this model allows access and participation by only the registered employees to ensure that no fraudulent activities occur as well as unapproved access. Employees can securely vote from anywhere with access being allowed. It has the implementation of Haar Cascades for quick facial detection and Convolutional Neural Networks for accurate facial verification, thus yielding a very high accuracy of 98%. Secure authentication along with real-time vote verification helps improve transparency, trust, and security in corporate governance. It provides a scalable and efficient solution for modern corporate elections while offering a new benchmark in digital voting technology.