Face Detection and Distance Measurement with Sound Alert

Augmented reality (AR), robotics (RC), and video conferencing are just a few of the many HCI fields that rely on face identification and camera distance estimate to provide a smooth user experience. This paper introduces a new method for improving the efficacy and precision of face recognition and interaction systems by integrating deep learning-based face identification algorithms with advanced camera distance estimation methods. In order to gain accurate and timely facial recognition. We train the camera to recognize facial landmarks, characteristics, and postures using annotated datasets and transfer learning algorithms. This allows for effective face identification regardless of the lighting or obstructions in the scene. Estimating camera distance is the focus of the second part of this research. In order to precisely estimate the distance between the camera and the identified face, we provide a multi-stage procedure that begins with calibrating the intrinsic characteristics of the camera. When these technologies are combined, distance estimate becomes more accurate, which allows for a more realistic experience in virtual worlds. We are considering utilizing a wide variety of technologies, including OpenCV, Eclipse, JAVA, and many more, to accomplish the aim.