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3D model of a human face. 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.
A face shape of vertices is defined as the vector containing the 3D coordinates of the vertices in a specified order, that is . A shape space is regarded as a d {\textstyle d} -dimensional space that generates plausible 3D faces by performing a lower-dimensional ( d ≪ n {\textstyle d\ll n} ) parametrization of the database. [ 2 ]
Facial recognition software at a US airport Automatic ticket gate with face recognition system in Osaka Metro Morinomiya Station. A facial recognition system [1] is a technology potentially capable of matching a human face from a digital image or a video frame against a database of faces.
DeepFace is a deep learning facial recognition system created by a research group at Facebook.It identifies human faces in digital images. The program employs a nine-layer neural network with over 120 million connection weights and was trained on four million images uploaded by Facebook users.
An eigenface (/ ˈ aɪ ɡ ən-/ EYE-gən-) is the name given to a set of eigenvectors when used in the computer vision problem of human face recognition. [1] The approach of using eigenfaces for recognition was developed by Sirovich and Kirby and used by Matthew Turk and Alex Pentland in face classification.
Image matches with the image stores in database. Any facial feature changes in the database will invalidate the matching process. [3] A reliable face-detection approach based on the genetic algorithm and the eigen-face [4] technique: Firstly, the possible human eye regions are detected by testing all the valley regions in the gray-level image.
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