AI based Security in computer vision and machine learning algorithms for hand sign recognition

In order to facilitate intuitive control and communication across a variety of areas, hand gesture recognition is an essential component of human-computer interaction systems. In this paper, a real-time hand gesture recognition system developed with the MediaPipe library, and convolutional neural networks (CNNs) is presented. The system utilizes a CNN model provided by MediaPipe for accurate detection of hand landmarks in camera feed images. Through extensive preprocessing and feature extraction, hand gestures, including finger gestures and palm orientations, are classified in real-time. The system offers dynamic selection between two modes: logging key points and logging point history, facilitating data collection for training or analysis purposes.Key features of the system include bounding rectangle calculation, landmark visualization, and gesture classification, providing users with immediate feedback on detected hand gestures. The modular design and parameter configurability make the system versatile for a wide range of applications, from interactive interfaces to sign language interpretation. The outcomes of the experiments showcase the practicality and efficacy of the suggested method, indicating that it has the potential to improve the experiences of people interacting with computers. Incorporating AI security measures, the system can significantly enhance security protocols in various applications. For instance, it can be used for gesture-based authentication, where specific hand gestures act as passwords to grant access to secure systems. This method can be further secured by combining it with traditional authentication techniques, creating a robust multi-factor authentication framework. Additionally, the real-time detection capability allows for immediate response to unauthorized access attempts, triggering alerts or lockdown procedures when unfamiliar gestures are detected.