Haar Cascade Classifier-based Real-Time Face Recognition and Face Detection

Face recognition is one of the most promising applications of image analysis that has gained a lot of attention recently. One may say that a sizable amount of facial recognition tasks involves face detection. Depending on how powerful it is, it can concentrate processing resources on the area of a picture that contains a face. Human faces vary in attitude, expression, location, orientation, and skin tone, making it difficult to identify faces in pictures. spectacles wearers or people with facial hair, camera gain, illumination, and image resolution. The main objective of face detection algorithms is to determine whether a face is visible in an image. The sophistication and accuracy of face detection and recognition have recently been the subject of numerous studies, but the Real-Time Face Detector created by the Viola-Jones Algorithm, which can accurately recognize faces in real-time, has caused a revolution in this area. Numerous detecting problems, including object, face, emotion, and face identification, have been successfully solved using neural networks. It is efficient to recognize objects using cascade classifiers based on Haar features. Face, eye, and smile detection using the Haar cascade classifier and facial recognition using OpenCV will both be implemented in this study utilizing the LBPH algorithm. If the individual is known, the accuracy of the image is predicted as well as face, eye, and grin detection using bounding boxes in the camera stream. The results are then displayed according to whether the person is known or one in the dataset.