Metrics details. It inherits advantages from traditional 2D face recognition, such as the natural recognition process and a wide range of applications. Moreover, 3D face recognition systems could accurately recognize human faces even under dim lights and with variant facial positions and expressions, in such conditions 2D face recognition systems would have immense difficulty to operate. This paper summarizes the history and the most recent progresses in 3D face recognition research domain.
3D Face Recognition: Technology and Applications
Three-dimensional face recognition - Wikipedia
Face recognition is the procedure of recognizing an individual from their facial attributes or features and belongs to the class of biometrics recognition methods. It employs data from 3D sensors that capture information about the shape of a face. Recognition is based on matching metadata extracted from the 3D shapes of faces. In an identification scenario, the matching is one-to-many, in the sense that a probe is matched against all of the gallery data to find the best match above some threshold. In an authentication scenario, the matching is one-to-one, in the sense that the probe is matched against the gallery entry for a claimed identity, and the claimed identity is taken to be authenticated if the quality of match exceeds some threshold.
Three-dimensional face recognition
New Approaches to Characterization and Recognition of Faces. Biometric systems for human recognition are an ongoing demand. Among all biometric technologies which are employed so far, face recognition is one of the most widely outspread biometrics. Its daily use by nearly everyone as the primary mean for recognizing other humans and its naturalness have turned face recognition into a well-accepted method.
Three-dimensional face recognition 3D face recognition is a modality of facial recognition methods in which the three-dimensional geometry of the human face is used. It has been shown that 3D face recognition methods can achieve significantly higher accuracy than their 2D counterparts, rivaling fingerprint recognition. This avoids such pitfalls of 2D face recognition algorithms as change in lighting, different facial expressions, make-up and head orientation.