Detecting eye corners using OpenCV involves identifying facial landmarks after detecting the face. First, use a face detection method like Haar cascades or DLIB’s pre-trained model to locate the face.
Next, apply a facial landmark detection algorithm, such as DLIB’s shape_predictor, to identify key points around the eyes. The corner points of the eyes are specific landmarks that can be extracted directly from the result.
These detected points can be used for applications like gaze estimation or emotion analysis. Fine-tuning parameters or using a pre-trained deep learning model for landmarks improves accuracy.