Method for recognizing human eye state based on built-in type hidden Markov model

Abstract

The invention discloses an eye state recognizing method based on built-in hidden Markov model, which includes the following steps: executing 2D-DCT transformation to the captured eye sample, executing analysis characteristic to the image after transformation, and searching the difference of the eyes open and close images after the 2D-DCT transformation; adopting a built-in hidden Markov model method to execute exercitation to the eye state image characteristic after the 2D-DCT transformation to obtain a categorizer; adopting the eye open and close state categorizer obtained by the above step to category the eye images to be recognized, firstly obtaining the observing vector sequence after the eye images to be recognized pass through the 2D-DCT transformation, and then adopting the built-in hidden Markov model method to calculate the likelihood values of the sequence produced by the eye open and close state categorizer, and judging the eye open and close state according to the likelihood value. The invention improves the robustness accuracy and real time performance of the arithmetic, reduces the calculation amount, thereby improving the eye state discriminating velocity.

Description

technical field [0001] The invention belongs to the application field of image processing and pattern recognition technology, in particular to a human eye state recognition method in driver fatigue detection technology. Background technique [0002] In the field of human eye state recognition, there are currently many methods, which can be roughly divided into two categories: human eye state recognition based on feature analysis and human eye state recognition based on pattern classification. Human eye state recognition based on feature analysis. The eye state is mainly determined by the inner and outer canthus, upper and lower eyelids, iris and sclera. There are three typical methods: grayscale template matching method, iris and eye white extraction method, Hough transform Pupil detection methods, all of which require high pixel accuracy in the human eye area, and the scope of application is limited. Human eye state recognition based on pattern classification is to judge t...

AI Technical Summary

Problems solved by technology

Human eye state recognition based on feature analysis. The eye state is mainly determined by the inner and outer canthus, upper and lower eyelids, iris and sclera. There are three typical methods: grayscale template matching method, iris and eye white extraction method, Hough transform Pupil detection method, which requires high pixel accuracy in the human eye area, and the scope of application is limited

Human eye state recognition based on pattern classification is to judge the human eye state according to the method of automatic learning rules or knowledge of samples. At present, there are mainly methods such as characteristic eye, neural network, SVM (Support Vector Machine), HMM (Hidden Markov Model), etc. Class methods generally require complex normalization processing such as zooming and rotation of the image, which requires a large amount of calculation, and also has certain requirements for pixel accuracy.

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