2019 21st International Conference on Advanced Communication Technology (ICACT) 2019
DOI: 10.23919/icact.2019.8701928
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A Study on Feature Extraction Methods Used to Estimate a Driver’s Level of Drowsiness

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Cited by 16 publications
(3 citation statements)
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“…They can easily divert the driver's attention and must be differentiated from each other for further processing from the research perspective. Feature extraction techniques are applied to extract the differentiated features [4] [55].…”
Section: ) Expression Differentiationmentioning
confidence: 99%
“…They can easily divert the driver's attention and must be differentiated from each other for further processing from the research perspective. Feature extraction techniques are applied to extract the differentiated features [4] [55].…”
Section: ) Expression Differentiationmentioning
confidence: 99%
“…These characteristics were the rate of eye closure, ECD, per closure, head positions, and yawning rate. Some limitations were also mentioned [15] [16].…”
Section: Introductionmentioning
confidence: 99%
“…Early research usually used head pose estimation [2][3], percentage of eyelid closures (PERCLOS) [4][5][6] or the number of yawns [7][8][9] to judge whether fatigue driving occurred. In recent years, various methods based on CNN, LSTM [22][23], and spatial-temporary graph convolution methods [24] have achieved good experimental results.…”
Section: Introductionmentioning
confidence: 99%