Face key part fatigue detection method

Abstract

Provided is a face key part fatigue detection method, comprising: first, utilizing a skin color model to detect a face area to provide initial positioning for an AAM; performing local human eyes and mouth tracking based on the AAM to obtain eye and mouth areas; utilizing a Canny operator to accurately position two areas to obtain fatigue detection parameters; and finally realizing fatigue detection according to a PERCLOS method. Face detection based on an HSV color model is not influenced by postures and angles, but is vulnerable to background interference; while the AAM has great face key point tracking effects, but is difficult to perform initial positioning; the method combines the HSV color model and thee AAM to realize eye and mouth accurate positioning and tracking. The method avoids body direct contact, employs AAM local texture search, reduces search time and obtains more accurate results compared with an ASM algorithm. Meanwhile, the method provides a driver spirit assessment module, can perform clear determination on driver states more easily.

Description

technical field [0001] The invention belongs to the technical field of image processing and pattern recognition, and in particular relates to a fatigue state detection method based on key parts of a driver's face. Background technique [0002] According to the statistics of China Rescue Equipment Network, the analysis and summary of traffic accidents in the first quarter of 2014 mentioned that there were 40,283 road traffic accidents involving casualties across the country, resulting in 10,575 deaths and direct property losses of 210 million yuan. Judging from the identified causes of accidents, there has been a significant rebound in accidents caused by fatigue driving, and the number of fatalities has increased by 12.1% year-on-year, which is one of the important reasons for the largest number of fatalities. Therefore, fatigue driving detection has become the frontier and hot spot of current domestic and foreign research. [0003] Precise localization of facial fatigue fe...

AI Technical Summary

Problems solved by technology

[0006] The method based on the global shape constraints of the face, such as the active shape model algorithm, uses the topological constraints of the face area to realize the positioning of the local area of ​​​​the eyes. Due to the introduction of the topological constraints of the face area, the positioning accuracy of this method is relatively high, but the posture adaptability is not good. Strong, unable to adapt to frequent changes in the driver's head posture during actual driving

[0007] Due to the defects in the above algorithms, many methods cannot achieve ideal results in practical applications, are not practical, and need to be improved

Images