Face detection

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Automatic face detection with OpenCV

Face detection is a computer technology that determines the locations and sizes of human faces in digital images. It detects face and ignores anything else, such as buildings, trees and bodies. Face detection can be regarded as a more general case of face localization. In face localization, the task is to find the locations and sizes of a known number of faces (usually one). In face detection, face is processed and matched bitwise with the underlying face image in the database. Any slight change in facial expression, e.g. smile, lip movement, will not match the face.

Face detection is also the psychological process by which we locate and attend to faces in a visual scene.[1] Research shows that our ability to detect faces is affected by a range of visual properties such as color and orientation. [2][3]

Definition and relation to other tasks[edit]

Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars.

Face-detection algorithms focus on the detection of frontal human faces. It is analogous to image detection in which the image of a person is matched bit by bit. Image matches with the image stores in database. Any facial feature changes in the database will invalidate the matching process.

Applications[edit]

Facial recognition[edit]

Face detection is used in biometrics, often as a part of (or together with) a facial recognition system. It is also used in video surveillance, human computer interface and image database management.

Photography[edit]

Some recent digital cameras use face detection for autofocus.[4] Face detection is also useful for selecting regions of interest in photo slideshows that use a pan-and-scale Ken Burns effect.

Marketing[edit]

Face detection is gaining the interest of marketers. A webcam can be integrated into a television and detect any face that walks by. The system then calculates the race, gender, and age range of the face. Once the information is collected, a series of advertisements can be played that is specific toward the detected race/gender/age.

An example of such a system is called OptimEyes and is integrated into the Amscreen digital signage system[5]

See also[edit]

References[edit]

  1. ^ Lewis, M.B. & Ellis, H.D. (2003). How we detect a face: A survey of the psychological evidence. International Journal of Imaging Systems and Technology, 13, 3-7. DOI: 10.1002/ima.10040
  2. ^ Lewis, M.B. & Edmonds, A.J. (2003). Face detection: Mapping human performance, Perception, 32, 903-920. DOI:10.1068/p5007
  3. ^ Lewis, Michael, and Andrew Edmonds. "Searching for faces in scrambled scenes." Visual Cognition 12.7 (2005): 1309-1336.DOI:10.1080/13506280444000535
  4. ^ "DCRP Review: Canon PowerShot S5 IS". Dcresource.com. Retrieved 2011-02-15. 
  5. ^ Tesco face detection sparks needless surveillance panic, Facebook fails with teens, doubts over Google+ | Technology | theguardian.com

External links[edit]