Detection method of eyes opening and closing ratio for driver's fatigue monitoring

Zhao, Qijie; Jiang, Jinlong; Lei, Zhigao; Yi, Jingang

doi:10.1049/itr2.12002

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…p �x i |y j � log 2 p�x i |y i � (7) The 𝑝𝑝(𝑥𝑥𝑥𝑥)as the prior probability of a random variable X, 𝑝𝑝(𝑥𝑥𝑥𝑥|𝑦𝑦𝑦𝑦) of the random variable Y under the condition of the a posteriori probability of X.…”

Section: A Improved D-s Algorithmmentioning

confidence: 99%

“…Cheng Bo [6] et al used D-S to carry out decision-level fusion of several fatigue discrimination indexes such as PERCLOS, longest eye closing time and percentage of stasis period in steering wheel, and the detection accuracy reached 91%, higher than those using a single index. Zhao [7] et al fused six fatigue sign subsets, such as blink, yawn, head posture and corresponding time and frequency, and used weighted feature subsets to determine the fatigue level.…”

Section: Introductionmentioning

confidence: 99%

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Face Fatigue Feature Detection Based on Improved D-S Model in Complex Scenes

Cai,

Liao,

Bai

et al. 2023

IEEE Access

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Fatigued driving is one of the main causes of road traffic accidents. In the process of fatigued driving detection, the evaluation based on a single sign is biased. To improve the adaptability and accuracy of fatigued driving detection, this paper proposes an improved D-S evidence theory-based algorithm for detecting facial fatigue signs. This algorithm uses the multi-thread-optimized Dlib to track and locate the image of the face, captures the 68 key points of the driver's face with reference to the Dlib open-source library, and narrows the target areas to the eyes and mouth regions. Based on the video stream, it calculates the horizontal and vertical ratios of the eyes and mouth to determine the fatigue sign subsets based on the EAR and MAR within a unit cycle, and calculates the Pitch angle after converting the head pose from 2D images to 3D models, which is used for determining the status of the head pose. The algorithm then fuses multiple feature subsets and uses the improved D-S evidence theory to optimize the weights of the three subsets to mitigate the influence of the "general conflict" and "one veto" problems, and to increase the temporal correlation coefficient of a single fatigue sign. Experimental results show that the improved D-S face detection algorithm can effectively solve the problems of lighting and partial occlusion in complex environments, with an accuracy rate of 93.8% for detecting facial fatigue signs.

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Section: A Improved D-s Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Face Fatigue Feature Detection Based on Improved D-S Model in Complex Scenes

Cai,

Liao,

Bai

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…It represents one of the most potent, natural, and universal signals available [2,3]. Due to its significant role in medical treatment [4], driver fatigue surveillance [5][6][7], sociable robotics [8][9][10], and many other human-computer interaction systems [11][12][13], automatic facial expression analysis has been the subject of several studies. Various facial expression recognition (FER) systems have been investigated in the field of machine learning and computer vision to encode expression information from facial representations.…”

Section: Introductionmentioning

confidence: 99%

Improving Facial Expression Recognition Using HOG with SVM and Modified Datasets Classified by Alexnet

Al-Atroshi,

Ali

2023

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Facial expressions are one of the communication ways between humans and their behavior can be determined through their facial expressions. Recently computer technology has been used to identify the facial expressions of people in order to predict their intentions. It remains a challenge for facial expression recognition to extract discriminative features from training sets with few labels because most deep learning-based algorithms primarily rely on spatial information and large labels. In this paper, we propose a system to classify seven types of facial expressions (Angry, Sadness, Surprise, Happiness, Fear, Neutral, and Disgust) instead of six, as in most previous research. In the proposed system, machine learning algorithms with deep learning are used to increase classification accuracy based on removing some unimportant facial regions. The support vector machine (SVM) algorithm is trained to detect the eyes and mouth regions from the face depending on histogram-oriented gradient (HOG) which is used as a features extractor. Then, merge the eyes and mouth regions for each image to create a new form of an image. After that, five different types of images are generated from the merged image named (RGB, HSV, Gray, Binary, and YCbCr). The images are fed one by one into the convolution neural network (CNN) algorithm. Finally, the voting process is used to select the most predictive class. The proposed system has been tested on three different types of datasets (KDEF, JAFFE, and FER2013) and the prediction accuracy in the system has reached more than 98% in all used datasets. The conclusion is that eliminating unimportant regions impacts the results of the classification accuracy.

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