Detecting driver drowsiness using feature-level fusion and user-specific classification
- Detecting driver drowsiness using feature-level fusion and user-specific classification
- 조재익; 이성주; 박강령; 김익재; 김재희
- Driver Drowsiness; Feature; Detection; Drowsiness detection system; Blink detection; Eye state classification; Feature-level fusion; User-specific classification
- Issue Date
- Expert systems with applications
- VOL 41, NO 4, 1139-1152
- Accurate classification of eye state is a prerequisite for preventing automobile accidents due to driver drowsiness. Previous methods of classification, based on features extracted for a single eye, are vulnerable to eye localization errors and visual obstructions, and most use a fixed threshold for classification, irrespective of variations in the driver’s eye shape and texture. To address these deficiencies, we propose a new method for eye state classification that combines three innovations: (1) extraction and fusion of features from both eyes, (2) initialization of driver-specific thresholds to account for differences in eye shape and texture, and (3) modeling of driver-specific blinking patterns for normal (non-drowsy) driving. Experimental results show that the proposed method achieves significant improvements in detection accuracy.
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