Cutting-edge Facial Authentication System in Matlab (CFAS-M)

Facial Authentication Systems have gained significant traction in recent years due to their convenience, security, and versatility in various applications. Facial recognition algorithms, including traditional methods like Eigen faces, Fisher faces, and Principal Component Analysis (PCA). Firstly, these methods heavily rely on handcrafted features, which may not adequately capture the complexities and variations present in facial images, leading to reduced accuracy. Additionally, traditional algorithms are often sensitive to changes in pose, lighting conditions, and facial expressions, making them less robust in real-world scenarios where such variations are common. Moreover, their limited discriminative power can result in higher rates of false acceptance or rejection, undermining the reliability of the system. Furthermore, traditional facial recognition algorithms struggle with noise and occlusions, as they may obscure critical facial features, further diminishing recognition accuracy. Lastly, these algorithms may lack scalability and adaptability, making them less suitable for deployment in diverse environments and populations where facial characteristics vary significantly. Using advanced MATLAB features, aims to serve as a foundation for secure authentication, seamless access control, and enhanced user experiences. The system consists of interconnected modules to ensure accuracy, robustness, and real-time capabilities. It begins with an advanced face detection module employing algorithms like Viola-Jones and deep learning networks to precisely locate faces within images and video streams. To enhance user interaction, an intuitive graphical user interface (GUI) created with MATLAB’s App Designer allows users to enroll new faces, perform recognition tasks, and interact with the system easily. The proposed work also prioritizes accountability and monitoring, featuring a logging and reporting module that records system activities, recognition outcomes, and user interactions. Additionally, a performance evaluation module measures system accuracy, precision, recall, and processing time to establish a quality benchmark