Human face eye region positioning method for fatigue driving detection

Abstract

The invention provides a human face eye region positioning method for fatigue driving detection. Under the condition that the head of a driver is relatively static and moves slowly, a human face detection process which needs a lot of calculating resources consumed by a cascade classifier can be omitted, and a face matching template is matched and a human face eye region is positioned quickly according to matching parameters of adjacent frame video images directly, while under the condition that the head of the driver relatively quickly moves, although the positioning efficiency is affected to a certain extent by reusing the cascade classifier for detecting the human face image region, no substantial influence is generated for the fatigue driving alarm function. Moreover, the relative position relationship of each face characteristic region in the human face image region is further determined and verified by virtue of the face matching template and the accuracy of a positioning result is ensured. By adopting a pixel gray level-based matching mode in matching, the data processing amount is small, the executing efficiency is relatively high and the human face eye region positioning speed is enhanced simultaneously when the relatively high accuracy is ensured.

Description

technical field [0001] The invention relates to the technical field of image processing and pattern recognition, in particular to a face and eye region positioning method for fatigue driving detection. Background technique [0002] Fatigue driving has become one of the main factors of traffic accidents. Fatigue driving detectors have been widely used as a detection and warning tool when drivers experience fatigue driving. Fatigue driving detection technology is the core technology of fatigue driving detector. At present, fatigue driving detection technology mainly includes detection based on human physiological signals (including EEG, ECG, skin potential, etc.), detection of vehicle status signals (speed, acceleration, lateral displacement, etc.), driver operation behavior (direction, accelerator and brake), etc. and other control situations) detection and driver facial image detection (closed eyes, blinking, yawning, head movement, etc.). Among them, eye activity feature ...

AI Technical Summary

Problems solved by technology

The method proposed in this patent has great limitations: on the one hand, the skin color model is only suitable for daytime, because the color difference caused by infrared supplementary light at night will make the skin color model completely invalid; on the other hand, in the driver's picture captured by the camera, not only The driver’s head is in motion, and the objects outside the window and other passengers in the car (especially the rear passengers) are in motion during the running of the vehicle. Therefore, there are many situations in the range of head motion detected by the difference between the adjacent frames, which cause the limitation of the detection error.

However, since the texture features of the eyes of the face are relatively small and complex, and their texture features are easy to change with the changes of facial expressions, the use of the texture features of the eyes of the face to realize the localization of the eyes of the face not only The imaging equipment is required to have high imaging quality, and the recognition processing equipment also needs to perform a large amount of data processing on the pixel data in the imaging image to distinguish these eye texture features. The processing process is relatively complicated and the amount of data processing is relatively large. Therefore, under the condition of limited data processing resources, the problems of low eye positioning efficiency and slow positioning speed are unavoidable

However, if the recognition efficiency and speed are improved by reducing the number of eye texture features used in the positioning and recognition process, human eye recognition errors will occur due to the many complex line textures in the driving area image, resulting in human face eye area When the positioning error is large

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