Human eye state detection method based on cascade classification and hough circle transform

Abstract

The utility model relates to a detection method of eye state based on cascade sort and Hough circle transform, belonging to the field of pattern recognition, which is characterized in the following steps: to acquire a face image; to carry out skin color segmentation in the YCbCr color space to acquire the position information of the skin color area using an ellipse skin model; to detect the rectangular eye area with the method of eye detecting window traversal with a cascade eye classifier; to merge a rectangle by rectangular merge method to get a merged eye rectangular linked list; to operate edge detection and binarization in turn on every rectangular eye area in the merged eye rectangular linked list with a Sobel operator to get an binary image; to detect the eye state of every binary image in turn with horizontal projection method; to further detect the eye state by Hough circle transform detection method if whether the present state is eye closure is not certain. The utility model increases the speed of eye detection, tracks and analyzes the eye state with the skin color segmentation, which is suitable for attention detection and fatigue detection.

Description

technical field [0001] The invention relates to a method in the field of pattern recognition, in particular to a human eye state detection method based on cascade classification and Hough circle transformation. Background technique [0002] Human eye detection refers to the detection of the human eye area in an image, and state detection refers to the analysis of the human eye area to determine whether the eyes are open or closed. Human eye detection has been widely researched and applied at home and abroad, including driver fatigue detection and so on. The methods of human eye detection can be summed up, including using the red eye effect to locate the iris position of the human eye to detect the human eye, using the characteristic that the gray value of the eye is lower than other places to detect the human eye, using the method of template matching to detect the human eye, and so on. However, the disadvantage of these methods is that the assumption is to detect human eye...

AI Technical Summary

Problems solved by technology

However, the disadvantage of these methods is that the assumption is to detect human eyes in a face image, and no state detection is performed on the detected human eyes. For images with complex backgrounds, the performance and effect of these methods not ideal

The operation process of this method is relatively complicated, non-real-time detection, and lack of analysis and judgment on the state of the eyes

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