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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. Such a system is typically employed to authenticate users through ID verification services, and works by pinpointing and measuring facial features from a given image.
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. [1] [2] The Facebook Research team has stated that ...
Current face recognition systems are designed to work with relatively small, static facial images. In the FRGC, high-resolution images consist of facial images with an average of 250 pixels between the centers of the eyes, which is significantly higher than the 40 to 60 pixels in current images.
Eigenface. An eigenface ( / ˈaɪɡən -/ 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.
Face ID is a facial recognition system designed and developed by Apple Inc. for the iPhone and iPad Pro.The system allows biometric authentication for unlocking a device, making payments, accessing sensitive data, providing detailed facial expression tracking for Animoji, as well as six degrees of freedom (6DOF) head-tracking, eye-tracking, and other features.
The Face Recognition Vendor Test ( FRVT) was a series of large scale independent evaluations for face recognition systems realized by the National Institute of Standards and Technology in 2000, 2002, 2006, 2010, 2013 and 2017. Previous evaluations in the series were the Face Recognition Technology (FERET) evaluations in 1994, 1995 and 1996.
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