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Face detection is a computer technology that determines the locations and sizes of human faces in arbitrary digital images. It detects facial features and ignores anything else, such as buildings, trees and bodies. 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 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, one does not have this additional information. Early face-detection algorithms focused on the detection of frontal human faces, whereas newer algorithms attempt to solve the more general and difficult problem of multi-view face detection. That is, the detection of faces that are either rotated along the axis from the face to the observer (in-plane rotation), or rotated along the vertical or left-right axis (out-of-plane rotation),or both. An ideal face detection system should be able to identify and locate all faces regardless of their positions, scale, orientation, lightning conditions, expressions and so on. Due to the large intra-class variations in facial appearances, face detection has been a challenging problem in the field of computer vision. There are many closely related problems with numerous applications of face detection:
  • Face localization aims to determine image position of a single face; a simplified detection problem with the assumption that an input image contains only one face.
  • Facial feature detection is to detect the presence and location of features, such as eyes, nose, nostrils, eyebrows, mouth, lips, ears, etc., with the assumption that there is only one face in an image.
  • Face recognition or face identification compares an input image against a database and reports a match, if any.
  • Face authentication verifies the claim of the identity of an individual in an input image.
  • Face tracking methods continuously estimate the location and possibly the orientation of a face in an image sequence in real time.
  • Facial expression recognition concerns identifying the affective states (happy, sad, disgusted, etc.) of humans.
Evidently, face detection is the first step in any automated system which solves the above problems and a robust and effective face detector system is essential.

Fig.1 Face detection

Face detection can be performed based on several different cues: skin colour (for faces in color images), motion (for faces in videos), facial/head shape and facial appearances, or a combination of them. However, detecting faces in black and white, still images with unconstrained, complex backgrounds is a complicated task. So far learning-based approaches have been most effective and have therefore attracted much attention the last years. Recently, Viola and Jones introduced an impressive face detection system capable of detecting frontal-view faces in real time. The desirable properties are partly attributed to the used AdaBoost learning algorithm. AdaBoost, from adaptive boosting, was rapidly made popular in the machine learning community when it was presented by Freund and Schapire about fifteen years ago.

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