Computers identify faces using algorithms that analyze image patterns to detect facial features. Traditional methods, like Haar cascades or Histogram of Oriented Gradients (HOG), rely on handcrafted feature detection to locate faces.
Modern approaches use deep learning models like Convolutional Neural Networks (CNNs), which automatically learn features such as eyes, nose, and mouth from training data. Models like MTCNN or RetinaFace further enhance accuracy by identifying facial landmarks and handling variations in pose and lighting.
These techniques are widely used in applications like facial recognition, security systems, and image tagging.