Face recognition algorithms detect human faces by identifying patterns in pixel data that correspond to facial features. Traditional methods use techniques like Haar cascades, which scan the image for specific patterns, or HOG, which detects edges and gradients.
Modern algorithms rely on deep learning models like MTCNN or RetinaFace, which are trained on large datasets to recognize facial landmarks and handle variations in pose, lighting, and expression. These models use convolutional layers to extract features and classify regions as faces.
These advancements enable face recognition systems to achieve high accuracy in diverse conditions.