Eye state detection method based on gradient and computer readable storage medium

Abstract

The invention discloses an eye state detection method based on gradient and a computer readable storage medium. The method comprises the following steps: acquiring a to-be-detected image in which twoeyes are in a preset initial state; sequentially acquiring a next frame of image to be detected as a current frame of image; calibrating an eye calibration point in the current frame image through a face detection algorithm and a face feature point calibration algorithm; calculating gradient information in the current frame image to obtain a gradient map corresponding to the current frame image; calibrating each eye calibration point in the current frame of image in the gradient map corresponding to the current frame of image according to the calibrated position of each eye calibration point in the previous frame of image to obtain the calibrated position of each eye calibration point in the current frame of image; and calculating an eye opening and closing angle corresponding to the sameeye according to the calibrated position of each eye calibration point of the same eye in the current frame image. According to the invention, the eye opening and closing angle can be accurately identified.

Description

technical field [0001] The invention relates to the technical field of eye state recognition, in particular to a gradient-based eye state detection method and a computer-readable storage medium. Background technique [0002] With the rapid development of image processing technology, face feature point calibration algorithms have achieved good results, and can accurately calibrate the contours of key organs such as eyes, nose, and mouth, such as dlib, openpose, openface, etc. Blink detection can be well used in human-computer interaction, such as unlocking mobile phones after face recognition, blinking to control robots, etc. Ideally, it becomes very simple to identify the state of the eye (blink detection) based on this type of algorithm, as long as the eye contour is used to easily identify the state of the eye. [0003] However, the calculation complexity of the openpose algorithm is very high. When the current high-performance discrete graphics card 1080ti runs this algo...

AI Technical Summary

Problems solved by technology

[0003] However, the calculation complexity of the openpose algorithm is very high. When the current high-performance discrete graphics card 1080ti runs this algorithm, it can only process more than a dozen images per second, not to mention the small smart terminal with relatively low computing power, which is difficult to achieve smooth to run

Although the facial feature point calibration of the dlib algorithm runs very fast, the calibrated eye contour is not accurate enough, and mis-calibration often occurs, resulting in errors in blink recognition; some researchers have also used some simple neural network structures to achieve similar results. The effect of openpose, but in the case of the user wearing glasses, the recognition effect is not good

Images