Eye and mouth state recognition method based on convolutional neural network

A convolutional neural network and state recognition technology, which is applied in the field of image recognition, can solve the problems of unbreakable recognition accuracy, few applicable scenarios, and low detection efficiency. It is friendly to transplantation and promotion, has a wide application range, and improves The effect of robustness

Pending Publication Date: 2017-03-08
TIANJIN POLYTECHNIC UNIV
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Problems solved by technology

Using multiple template matching, the detection efficiency is low and the real-time performance is poor
The second type of method uses the gray projection curve of the iris area of ​​the eye to judge the state of the eye, which has higher requirements on lighting and is less applicable to the scene.
The third type of method uses eye opening and closing detection based on the combination of LBP features and SVM classifiers, which has certain limitations for drivers wearing sunglasses and posture changes, and has poor robustness.
The fourth type of method uses eye state recognition base

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  • Eye and mouth state recognition method based on convolutional neural network
  • Eye and mouth state recognition method based on convolutional neural network
  • Eye and mouth state recognition method based on convolutional neural network

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[0030] In order to enable your examiners to further understand the structure, features and other purposes of the present invention, the attached preferred embodiments are now described in detail as follows. The described preferred embodiments are only used to illustrate the technical solutions of the present invention, not to limit the present invention. invention.

[0031] Process flow of the present invention such as figure 1 As shown, firstly, the face area of ​​interest is detected based on the haar feature combined with the AdaBoost algorithm (or other methods), and the face feature points are detected based on the preliminary face detection results by a combination of random forest and linear regression, and Extract the eyes and mouth area; then according to the basic structure of the convolutional neural network convolutional layer, downsampling layer and fully connected layer and the Lenet5 network structure, the neural network is optimized by convolution of the local ...

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Abstract

The invention relates to an eye and mouth state recognition method based on a convolutional neural network. SR-Net (State recognition nets) designed through the method learn a large amount of different eye and mouth state samples. Face state recognition can be regarded as eye state recognition and mouth state recognition. Eye and mouth states can be recognized and classified more accurately. Due to the convolutional neural network, extraction of artificial features is avoided, and recognition on eye and mouth states has high robustness. Due to the method of the invention, the recognition rate in a sunglass wearing condition is enhanced, the average accuracy for eye state recognition is improved to more than 98.41%, the average recognition rate for eye states without glasses is 98.92%, and the average recognition rate for the mouth states is 99.33%.

Description

technical field [0001] The invention relates to an eye and mouth state recognition method based on a convolutional neural network. The method can adapt to illumination changes and glasses blocking situations, belongs to the technical field of image recognition, and can be applied to determine the driver's fatigue state. Background technique [0002] Eye and mouth state recognition can be considered equivalent to the recognition of human face and face state. It is an important content in the field of image recognition and has a direct impact on information security, automatic driving and other technologies. According to the report of the National Center for Statistics and Analysis of the United States, fatigue driving is one of the important causes of traffic accidents. Therefore, the research on driver fatigue detection technology has important significance for preventing traffic accidents. In recent years, with the improvement of computer hardware level, the fatigue detecti...

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Application Information

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IPC IPC(8): G06K9/00G06N3/02G06K9/32
CPCG06N3/02G06V40/165G06V40/171G06V10/25
Inventor 耿磊梁晓昱肖志涛张芳吴骏苏静静
Owner TIANJIN POLYTECHNIC UNIV
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