Face detection method and system based on image on-line learning

Abstract

The invention discloses a face detection method and system based on image on-line learning. The face detection method based on image on-line learning comprises the steps of (1) preprocessing, wherein illumination compensation and graying processing are carried out on an image to be detected, image enhancement is carried out, nonlinear smooth filtering is carried out, denoising is carried out on the image, gray levels of pixels are normalized to obtain a high-quality gray level image, and then size normalization processing and edge detection processing are carried out; (2) carrying out face gesture detection, wherein the positions of the human eyes are determined, a human face area is divided, the rotation angle of a human face in the pitching dimension, the rotation angle of the human face in the depth dimension and the rotation angle of the human face in the plane dimension are detected, and whether the human face has an expression or not is automatically judged; (3) carrying out face detection, wherein the position of the human face in the image is determined, organs of the human face are located, and gray features of the image are selected, and are transmitted to a detection template which is trained in an off-line mode to carry out judgment; (4) updating, wherein the image which is processed through detection serves as a new sample to be applied to learning of a multi-layer cascade AdaBoost classifier, and the weight of set characteristic values of the multi-layer cascade AdaBoost classifier is updated. According to the face detection method and system based on image on-line learning, the accuracy rate of face detection can be improved.

Description

technical field [0001] The invention belongs to the technical field of face recognition, and relates to a face detection method, in particular to a face detection method based on image online learning; meanwhile, the invention also relates to a face detection system based on image online learning. Background technique [0002] At present, in the field of image information processing for face recognition, there are multiple research directions, including image preprocessing, pose detection, face tracking, expression recognition, feature extraction, face detection, etc., all of which involve image learning. At the same time, offline learning is currently the most widely used method, but this method is difficult to adapt to changes in goals, especially in complex environments. In addition, it takes a lot of energy to obtain a sufficiently rich sample set through the existing recognition technology, which greatly limits the practicability of the algorithm. [0003] In view of t...

AI Technical Summary

Problems solved by technology

At the same time, offline learning is the most widely used at present, but this method is difficult to adapt to changes in goals, especially in complex environments.

In addition, it takes a lot of energy to obtain a sufficiently rich sample set through the existing recognition technology, which greatly limits the practicability of the algorithm

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