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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.[1] Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene.[2]

Definition and related algorithms[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.

A reliable face-detection approach based on the genetic algorithm and the eigen-face[3] technique:[4]

Firstly, the possible human eye regions are detected by testing all the valley regions in the gray-level image. Then the genetic algorithm is used to generate all the possible face regions which include the eyebrows, the iris, the nostril and the mouth corners.

Each possible face candidate is normalized to reduce both the lightning effect, which is caused by uneven illumination; and the shirring effect, which is due to head movement. The fitness value of each candidate is measured based on its projection on the eigen-faces. 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.


Facial motion capture[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.


Some recent digital cameras use face detection for autofocus.[5] Face detection is also useful for selecting regions of interest in photo slideshows that use a pan-and-scale Ken Burns effect.

Modern appliances also use smile detection to take a photograph at an appropriate time.


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.[6]

See also[edit]


  1. ^
  2. ^ Lewis, Michael B; Ellis, Hadyn D (2003), "How we detect a face: A survey of psychological evidence", International Journal of Imaging Systems and Technology, 13: 3, doi:10.1002/ima.10040
  3. ^
  4. ^ 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. 34 (10): 1993. doi:10.1016/s0031-3203(00)00134-5.
  5. ^ "DCRP Review: Canon PowerShot S5 IS". Retrieved 2011-02-15.
  6. ^ Tesco face detection sparks needless surveillance panic, Facebook fails with teens, doubts over Google+ | Technology |

External links[edit]


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