TY - JOUR
T1 - Detecting driver drowsiness using feature-level fusion and user-specific classification
AU - Jo, Jaeik
AU - Lee, Sung Joo
AU - Park, Kang Ryoung
AU - Kim, Ig Jae
AU - Kim, Jaihie
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Blink detection
KW - Drowsiness detection system
KW - Eye state classification
KW - Feature-level fusion
KW - User-specific classification
UR - http://www.scopus.com/inward/record.url?scp=84888363097&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2013.07.108
DO - 10.1016/j.eswa.2013.07.108
M3 - Article
AN - SCOPUS:84888363097
SN - 0957-4174
VL - 41
SP - 1139
EP - 1152
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 4 PART 1
ER -