After a number of iterations, all the face candidates with a high fitness value are selected for further verification. The fitness value of each candidate is measured based on its projection on the eigen-faces.

Įach possible face candidate is normalized to reduce both the lighting effect, which is caused by uneven illumination and the shirring effect, which is due to head movement. 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. Ī reliable face-detection approach based on the genetic algorithm and the eigen-face technique:įirstly, the possible human eye regions are detected by testing all the valley regions in the gray-level image. Any facial feature changes in the database will invalidate the matching process. Image matches with the image stores in database. It is analogous to image detection in which the image of a person is matched bit by bit. where is the located?įace-detection algorithms focus on the detection of frontal human faces. are there any human faces in the collected images or video? 2. Examples include upper torsos, pedestrians, and cars.įace detection simply answers two question, 1. 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. Definition and related algorithms įace detection can be regarded as a specific case of object-class detection.

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