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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.
Face detection. Automatic face detection with OpenCV. Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. [1] Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. [2]
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 ...
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.
Anatomical terminology. [ edit on Wikidata] The fusiform face area ( FFA, meaning spindle-shaped face area) is a part of the human visual system (while also activated in people blind from birth [1]) that is specialized for facial recognition. [2] It is located in the inferior temporal cortex (IT), in the fusiform gyrus ( Brodmann area 37 ).
Facial perception is an individual's understanding and interpretation of the face. Here, perception implies the presence of consciousness and hence excludes automated facial recognition systems. Although facial recognition is found in other species, [1] this article focuses on facial perception in humans. The perception of facial features is an ...
Super recogniser. " Super recogniser " is a term coined in 2009 by Harvard and University College London researchers for people with significantly better-than-average face recognition ability. [1] [2] Super recognisers are able to memorise and recall thousands of faces, often having seen them only once. [3]
FaceNet is a facial recognition system developed by Florian Schroff, Dmitry Kalenichenko and James Philbina, a group of researchers affiliated with Google. The system was first presented at the 2015 IEEE Conference on Computer Vision and Pattern Recognition. [1] The system uses a deep convolutional neural network to learn a mapping (also called ...