Face recognition algorithms analyze facial features to identify or verify individuals. The process typically involves four steps: detection, alignment, feature extraction, and matching.
First, the algorithm detects a face in an image or video using techniques like Haar cascades or deep learning-based detectors. Next, the face is aligned to a standard orientation, accounting for rotation or tilt, to ensure consistent feature extraction.
The algorithm then converts the facial features into numerical representations called embeddings. These embeddings are generated using neural networks, such as convolutional neural networks (CNNs), which learn distinctive patterns like the spacing between eyes or the shape of the nose.
Finally, the embeddings are matched against a database of known faces. Similarity metrics, like cosine similarity or Euclidean distance, determine the degree of match. If the similarity surpasses a threshold, the identity is confirmed.
Face recognition algorithms are widely used in security systems, access control, and authentication processes. However, their effectiveness depends on the quality of training data and can be impacted by factors like lighting, facial expressions, or occlusions. Advances in deep learning continue to enhance accuracy and robustness in diverse scenarios.