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Fatigue driving detection method based on Kinect and face recognition

A technology for face recognition and fatigue driving, which is applied in the field of face recognition and can solve problems such as high computing requirements

Inactive Publication Date: 2015-04-08
TIANJIN UNIV
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Problems solved by technology

Another human eye recognition and tracking algorithm based on the Hough transform is to decompose facial expressions according to the movement of facial feature points. This algorithm has been valued by many people due to its high accuracy, but the disadvantage is that it is calculated High standard

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  • Fatigue driving detection method based on Kinect and face recognition
  • Fatigue driving detection method based on Kinect and face recognition
  • Fatigue driving detection method based on Kinect and face recognition

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Embodiment Construction

[0046] The advantage of Kinect is that its SDK includes face recognition and tracking SDK, which can detect and locate faces more accurately. The impact of the inaccurate positioning of the human eye on the experimental results is eliminated. When extracting features from the human eye, first perform grayscale processing to remove color interference, then perform binarization processing to eliminate the interference of different brightness on the experimental results, and finally judge and detect the time of blinking and closing eyes. This paper also adds the detection of yawning as a condition for judging fatigue. With the help of the Kinect platform, this paper focuses on face recognition and detection, and it is of great research significance to judge fatigue driving through a series of feature detection on the face.

[0047] The technical solutions and embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawi...

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Abstract

The invention discloses a fatigue driving detection method based on Kinect and face recognition; the method comprises the following steps: step one, shooting a video by a Kinect camera, implementing face recognition tracing analysis, so as to obtain a 3D mask of a user face; step two, extracting 121 characteristic points of face on the 3D mask obtained by the Kinect face recognition; extracting and recording characteristic points of the eye image area and the mouth image area from the face characteristic points; step three, marking the eye area in the recognized face image, and extracting the eye area to a new drawing board, and implementing graying process and binaryzation process to the eye image; step four, introducing criterions for judging fatigue driving, i.e., rate of change about eye blinking frequency and detection of eye closing time, and detection about yawning. The fatigue driving detection method based on Kinect and face recognition has a certain scientific research value and a use for reference about the development of fatigue driving monitoring method based on video which is still at the initial stage in our country at present; and the fatigue driving detection method based on Kinect and face recognition has a promoting significance about the research project of fatigue driving.

Description

technical field [0001] The invention relates to face recognition technology, in particular to a fatigue driving detection method based on Kinect and face recognition. Background technique [0002] Fatigue driving has become a major cause of road traffic accidents, and driving fatigue will affect the driver's attention, feeling, perception, thinking, judgment, will, decision and movement. As an important cause of frequent traffic accidents, how to prevent and detect fatigue driving has become a hot topic and research direction in the scientific field. At present, the research and development of video-based fatigue driving monitoring system in China is still in the initial stage. Therefore, research on fatigue driving has great scientific value and significance. [0003] There are roughly three types of technologies for fatigue driving detection in the world today, one is physical sensors, the other is physical signals such as EEG, and the third is based on computer vision. ...

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Application Information

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IPC IPC(8): G08B21/06G06K9/00
Inventor 杨天培孟昭鹏陶文源
Owner TIANJIN UNIV
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