Human face-tracking preprocessing method and video-based intelligent health monitoring system

Abstract

The invention discloses a human face-tracking preprocessing method and application. The method comprises a step (1) of recovering an image collected by a camera to a color below daily illumination color temperature (5500 k) through color balance processing; a step (2) of causing the whole color-balanced image to undergo noise filtering, and adopting median filtering processing for the noise filtering; a step (3) of utilizing skin color segmentation to cut out of background the part of the image belonging to the human skin color; a step (4) of further performing constraint handling, and only sending areas meeting the human face normal form proportion to human face detection in next step, or else filtering out the area; a step (5) of performing AdaBoost detection on each skin color area remaining after the operation through the step (4). Through the preprocessing method, the detection range of an AdaBoost algorithm can be effectively narrowed down, and the detection speed is improved.

Description

technical field [0001] The invention relates to the field of human face intelligent recognition. Background technique [0002] The research on face detection can be traced back to the 1970s, and the early researches were mainly devoted to template matching, subspace method, deformed template matching, etc. Recent research on face detection mainly focuses on data-driven learning methods, such as statistical model methods, neural network learning methods, statistical knowledge theory and support vector machine methods, Markov random field-based methods, and skin color-based face detection methods. detection. Most of the face detection methods currently used in practice are based on the Adaboost learning algorithm. [0003] In the prior art, in order to extract facial information, the AdaBoost algorithm is first used to extract the face from the entire picture, but when the face is detected on the full picture, the original AdaBoost algorithm will cause the detection speed to...

AI Technical Summary

Problems solved by technology

[0003] In the prior art, in order to extract facial information, the AdaBoost algorithm is first used to extract the face from the entire picture, but when the face is detected on the full picture, the original AdaBoost algorithm will cause the detection speed to be too slow due to the large detection range. Causes problems such as lack of real-time performance in the detection process

Images