3D face recognition creates a three-dimensional model of a face to improve accuracy and robustness. Unlike 2D face recognition, which relies on flat images, 3D methods capture depth information using specialized sensors like structured light cameras or stereo vision systems.
The process begins by collecting a 3D face scan, which includes data on surface geometry and contours. The system creates a 3D point cloud or a depth map representing the face. These models are invariant to lighting and pose, addressing some limitations of 2D recognition.
Next, the algorithm extracts features from the 3D model, such as the curvature of facial contours or distances between key points. These features are transformed into embeddings—numerical representations that encode the face’s unique characteristics.
During matching, the embeddings are compared to those in a database using similarity metrics. Because 3D data captures more detail, it is less affected by changes in facial expressions or angles, making it highly accurate.
3D face recognition is used in high-security applications, such as biometric authentication and airport security, where precision is critical. However, it requires more computational resources and specialized hardware, which may increase implementation costs.