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Cognitive psychology. 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 ...
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.
Heterophoria is the misalignment of the visual axis such that one or both eyes are not properly fixated to an object of interest. When the visual axis is misaligned in such a way, it is corrected by the fusional vergence system. Diagnosis. The cross-cover test, or alternating cover test is usually employed to detect heterophoria.
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.
Incomitant strabismus cannot be fully corrected by prism glasses, because the eyes would require different degrees of prismatic correction dependent on the direction of the gaze. Incomitant strabismus of the eso- or exo-type are classified as "alphabet patterns": they are denoted as A- or V- or more rarely λ -, Y- or X-pattern depending on the ...
Esophoria is an eye condition involving inward deviation of the eye, usually due to extra-ocular muscle imbalance. It is a type of heterophoria. Cause. Causes include: Refractive errors; Divergence insufficiency; Convergence excess; this can be due to nerve, muscle, congenital or mechanical anomalies.
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.
3D face recognition has the potential to achieve better accuracy than its 2D counterpart by measuring geometry of rigid features on the face. This avoids such pitfalls of 2D face recognition algorithms as change in lighting, different facial expressions, make-up and head orientation.
The face inversion effect occurs when, compared to other objects, it takes a disproportionately longer time to recognise faces when they are inverted as opposed to upright. [3] [4] Faces are normally processed in the special face-selective regions of the brain, such as the fusiform face area. [5]
The Face Recognition Grand Challenge (FRGC) was a project that aimed to promote and advance face recognition technology to support existing face recognition efforts within the U.S. Government. The project ran from May 2004 to March 2006 and was open to face recognition researchers and developers in companies, academia, and research institutions ...