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

  • Authors:

  • Jaeik Jo;Sung Joo Lee;Kang Ryoung Park;Ig-Jae Kim;Jaihie Kim

  • Affiliations:

  • School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Republic of Korea;School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Republic of Korea;Division of Electronics and Electrical Engineering, Dongguk University, Seoul 100-715, Republic of Korea;Imaging Media Research Center, Korea Institute of Science and Technology, Seoul 136-130, Republic of Korea;School of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Republic of Korea and Sunway University, 46150 Petaling Jaya, Selangor, Malaysia

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2014

Quantified Score

Hi-index 12.05

Visualization

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.