Multi-feature fusion fatigue detection method based on deep learning and machine learning

Abstract

The invention discloses a multi-feature fusion fatigue detection method based on deep learning and machine learning. The method comprises the following steps: S1, data acquisition: collecting a fatigue face image; S2, constructing an expression recognition data set; S3, extracting an attention feature map: inputting the expression recognition data set into a deep residual network to obtain the attention feature map, then adding the attention feature map into a newly constructed new data set containing fatigue facial expressions, and constructing a data set of the attention feature map; S4, inputting the expression recognition data set containing the attention feature map into a 19-layer convolutional neural network VGG19 for training; S5, extracting traditional fatigue features; S6, extracting a deep learning confidence coefficient; and S7, fusing multiple features to train a machine learning classifier. According to the method, based on the expression recognition model guided by the attention feature map, the deep learning network is used, the attention is focused on the eye and mouth areas with the most abundant features on the face, and the recognition precision of expression recognition can be improved.

Description

technical field [0001] The invention belongs to the field of expression recognition and fatigue detection of computer vision, specifically relates to a method for introducing an attention mechanism into expression recognition, and in particular to a fatigue detection method based on multi-feature fusion of deep learning and machine learning. Background technique [0002] Fatigue state is a very common state in our life. When people are in a state of fatigue, they will appear inattention, low work efficiency and other performances, which will have a great impact on our work and life. For office workers who need to sit for a long time, the fatigue detection system can detect their emotional and mental state in real time and give corresponding suggestions. Sometimes, fatigue can easily lead to major accidents. For example, drivers who drive with fatigue are prone to major traffic accidents. Therefore, it is necessary to perform fatigue detection during work for drivers, office ...

AI Technical Summary

Problems solved by technology

Although the fatigue detection algorithm based on deep learning does not require manual feature extraction, it is similar in principle to the traditional algorithm. There are algorithms that use convolutional neural network and recurrent neural network LSTM to detect whether the human eye is closed, but these methods do not consider multiple indicators, the reliability is not high

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