Face Recognition using Video and Live Stream a Machine Learning Approach

One of the top computer vision technologies at the moment is facial recognition. In computer vision, recognising faces is always an extremely challenging process due to lighting, stance, and facial expression. Target objects are tracked by face recognition in live streaming or through video. It is a system application that, in simplest terms, automatically recognises a person from a live capture through camera or video frame. We suggested an automatic face recognition system in this project. When the person in front of the camera recognises him, this application, which is based on face detection, feature extraction, and recognition algorithms, automatically detects the human face. Although the camera is continuously identifying the face in every frame, we employed the Haar cascade classifier to detect human faces. We have utilised the CNN method to train the system using the dataset that is currently accessible, and using real-time face capture, we will store the face data in a database. It was trained using the LBPH recognizer. Face recognition has done through Harr cascade algorithm.