Cameras detect faces using algorithms like Haar cascades or deep learning-based methods like SSD or YOLO. These algorithms analyze pixel patterns to identify regions resembling facial features.
Modern approaches use deep learning models, such as MTCNN or RetinaFace, which are trained on large datasets to improve accuracy and detect faces in varying conditions. They recognize facial landmarks, such as eyes, nose, and mouth, to confirm the presence of a face.
These techniques enable applications like facial recognition, user authentication, and real-time facial expression analysis.