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

A monitoring system and preprocessing technology, applied in the field of intelligent face recognition, can solve the problems of lack of real-time performance and slow detection speed in the detection process

Active Publication Date: 2016-08-17
NANJING UNIV
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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 pictu

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

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Embodiment Construction

[0030] Technical term used in the present invention:

[0031] AdaBoost algorithm: In 2001, Viola Johns proposed a real-time face detection algorithm based on the Boosting algorithm;

[0032] Camshift: It is a tracking algorithm with an adaptive window size added on the basis of Meanshift, which is simple and has high real-time performance;

[0033] PCA: The local information is extracted by the Fourier transform of the signal, and the window function of time localization is introduced. Now the window Fourier transform is called the Gabor transform; the feature information of the face is extracted through the Gabor transform, and then the dimensionality is reduced through the PCA algorithm. Get the principal components in the Gabor transform, and then use different classifiers or machine learning methods for expression extraction

[0034] Corner detection: Detect the points in the image where the brightness of the two-dimensional image changes sharply or the point with the max...

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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...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/40G06T7/00
CPCG06V40/162G06V40/174G06V40/16G06V40/168G06V10/30
Inventor 季晓勇禹珍张轩张迎冯正伟夏煦菁
Owner NANJING UNIV
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