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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 developed more, but holds similarities to the brains of birds and lower mammals.
The face-space framework is a psychological model that explains how (adult) humans process and store facial information, which we use for facial recognition. It is multidimensional, with each dimension categorised by certain facial features, some of which may be: face shape, hair colour and length, distance between the eyes, age and masculinity.
Bruce & Young Model of Face Recognition, 1986. One of the most widely accepted theories of face perception argues that understanding faces involves several stages: [7] from basic perceptual manipulations on the sensory information to derive details about the person (such as age, gender or attractiveness), to being able to recall meaningful details such as their name and any relevant past ...
Satellite photograph of a mesa in the Cydonia region of Mars, often called the "Face on Mars" and cited as evidence of extraterrestrial habitation. Pareidolia (/ ˌ p ær ɪ ˈ d oʊ l i ə, ˌ p ɛər-/; [1] also US: / ˌ p ɛər aɪ-/) [2] is the tendency for perception to impose a meaningful interpretation on a nebulous stimulus, usually visual, so that one detects an object, pattern, or ...
Spatial intelligence is an area in the theory of multiple intelligences that deals with spatial judgment and the ability to visualize with the mind's eye. It is defined by Howard Gardner as a human computational capacity that provides the ability or mental skill to solve spatial problems of navigation, visualization of objects from different angles and space, faces or scenes recognition, or to ...
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.
"The Face" near the Moon's South Pole. The Face on Moon South Pole is a region on the Moon (81.9° south latitude and 39.27° east longitude) that was detected automatically in an image from the Lunar Reconnaissance Orbiter by a computer system using face recognition technologies, [1] as a result of a project that was part of the International Space App Challenge 2013 Tokyo.
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]