Face detection

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This article is about Face detection. For Face recognition system, see Facial recognition system. For Human face perception, see Face perception.
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. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene.

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.

Common Face-detection algorithms might proceed in following way:[1]

Firstly, the possible human eye regions are detected by testing all the valley regions in the gray-level image. A pair of eye candidates are selected by means of the genetic algorithm [2] to form a possible face candidate. The fitness value of each candidate is measured based on its projection on the eigen faces.[3] In order to improve the level of detection reliability, each possible face region is normalized for illumination; the shirring effect, when the head is tilted, is also considered as well.After a number of iterations, all the face candidates with a high fitness value are selected for further verification. At this stage, the face symmetry is measured and the existence of the different facial features is verified for each face candidate. The facial features are determined by evaluating the topo-graphic relief of the normalized face regions. The facial features extracted include the eyebrow, the iris, the nostril, and the mouth corner.


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.


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.


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 OptimEyes and is integrated into the Amscreen digital signage system.[5] The most recent innovation in the field was by, Miami Beach based, AdMobilize. Their AdBeacon, became the world's first 'plug and measure real time analytics device', allowing for any retailer to get the same facial detection technology large advertisers are using. They've also coined the term pay-per-face.[6]

See also[edit]


  1. ^ Kwok-Wai Wong, Kin-Man Lam, Wan-Chi Siu (2001). "An efficient algorithm for human face detection and facial feature extraction under different conditions" (PDF). Pattern Recognition. 
  2. ^ Goldber, David E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Boston, MA, USA: Addison-Wesley Longman Publishing Co. ISBN 0201157675. 
  3. ^ M. Turk and A. Pentland (Winter 1991.). "Eigenfaces for recognition". Cognitive Neuroscience.  Check date values in: |date= (help)
  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
  6. ^ [1]

External links[edit]