Search results
Results from the WOW.Com Content Network
Pattern Recognition is a novel by science fiction writer William Gibson published in 2003. Set in August and September 2002, the story follows Cayce Pollard , a 32-year-old marketing consultant who has a psychological sensitivity to corporate symbols.
Physiognomy as it is understood today is a subject of renewed scientific interest, especially as it relates to machine learning and facial recognition technology. [6] [7] [8] The main interest for scientists today are the risks, including privacy concerns, of physiognomy in the context of facial recognition algorithms.
Aged 32 during the events of Pattern Recognition, Cayce lives in New York City.Though named by her parents after Edgar Cayce, she pronounces her given name "Case". [4] She is a freelance marketing consultant, a coolhunter with an unusual intuitive sensitivity for branding, [5] manifested primarily in her physical aversion to particular logos and corporate mascots. [6]
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]
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.
Semantic memory, which is used implicitly and subconsciously, is the main type of memory involved in recognition. [2] Pattern recognition is crucial not only to humans, but also to other animals. Even koalas, which possess less-developed thinking abilities, use pattern recognition to find and consume eucalyptus leaves. The human brain has ...
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.
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.