The invention relates to a
face detection method based on an
Adaboost algorithm, which comprises the steps of preprocessing a face image, performing
skin color segmentation in an
YCbCr color space, acquiring a face candidate region, further performing
face detection according to the
Adaboost algorithm, and matching a screened face region with a face template, wherein face image preprocessing comprises
grayscale normalization, light compensation, filtering and
noise reduction and geometric normalization;
skin color segmentation comprises
color space conversion,
skin color segmentation performed by using a
color scale model, and further face candidate region screening according to the area of a
skin color connected region and the length-
width ratio of an external rectangle; the
Adaboost face detection algorithm comprises that weak classifiers are trained, the weak classifiers are combined into strong classifiers, and the strong classifiers are connected in series to form a
cascade classifier; and face
template matching comprises that the matching degree between the candidate face region acquired through
processing and a face template is measured by using the weighted
Euclidean distance. The face detection method improves the face detection speed and accuracy, and is easy to implement and operate, stable and reliable.