Face detection and face recognition are key computer vision techniques that leverage deep learning to identify and analyze human faces. While they are related, each serves a distinct purpose in the modern AI ecosystem.
Face detection algorithms are designed to locate and extract faces from images or videos. These systems generate bounding boxes around faces in the input data, isolating them for further analysis. Deep learning models, particularly Convolutional Neural Networks (CNNs), are widely used in face detection due to their ability to capture intricate patterns under various conditions.
Face recognition algorithms go a step further, aiming to identify or verify individuals by analyzing the unique features of each face. These systems extract distinguishing attributes and compare them to a database of known faces. CNNs are extensively used due to their ability to extract highly discriminative features across varying lighting and expressions.
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