Detecting driver drowsiness using feature-level fusion and user-specific classification

Title
Detecting driver drowsiness using feature-level fusion and user-specific classification
Authors
조재익이성주박강령김익재김재희
Keywords
Driver Drowsiness; Feature; Detection; Drowsiness detection system; Blink detection; Eye state classification; Feature-level fusion; User-specific classification
Issue Date
2014-03
Publisher
Expert systems with applications
Citation
VOL 41, NO 4, 1139-1152
Abstract
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.
URI
http://pubs.kist.re.kr/handle/201004/46577
ISSN
09574174
Appears in Collections:
KIST Publication > Article
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