Fatigue state detection method based on micro-expressions

Abstract

The invention discloses a fatigue state detection method based on micro-expressions, and aims to solve the problems in existing fatigue detection. The method comprises the following specific steps: step 1, capturing real-time facial micro-expressions of a tester, storing the real-time facial micro-expressions, extracting the real-time facial micro-expressions into a plurality of feature points, and then performing data screening to obtain personal facial data; step 2, collecting facial images of the tester in a waking state and a slight fatigue state, and constructing a personalized micro-expression fatigue recognition model; and step 3, substituting the personal face data into the personalized micro-expression fatigue recognition model for analysis. The method can effectively detect whether a tester is in a fatigue state or not, and can detect an independent individual; compared with a traditional detection means, the method has the advantages of being high in detection precision, free of contact, high in individuation and capable of updating the fatigue condition according to the real-time state of a tester, and popularization is convenient.

Description

technical field [0001] The invention relates to the field of fatigue detection, in particular to a fatigue state detection method based on micro-expressions. Background technique [0002] With the rapid development of my country's economic level and the development of science and technology, more and more families have private cars. It is very common for a family to have multiple vehicles. Cars have become a daily means of transportation for people. A car is a non-track-bearing vehicle driven by power and has 4 or more wheels, mainly used to carry people and goods, and tow vehicles that carry people and goods. [0003] Cars have brought convenience to our lives, but in recent years car accidents have occurred frequently, and fatigue driving is one of the common factors. To reduce such accidents, drivers are tested. At present, whether fatigue is judged mainly by detecting the physiological characteristics and facial features of the personnel. However, detection methods re...

AI Technical Summary

Problems solved by technology

However, detection methods relying on physiological characteristics such as brain waves require installing detectors in the car, which is cumbersome to implement, and the driver's seat is too small to install such detection equipment; while the detection of facial features is mainly based on human eye blinking frequency to judge fatigue , but everyone has specificity, and the general fatigue standard set based on changes in eye blinking frequency cannot truly and accurately reflect changes in fatigue

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