Affiliations: [a] College of Transportation, Southeast University, Nanjing, PR China | [b] School of Engineering, Griffith University, Brisbane, QLD, Australia
Abstract: To develop the human-centric driver fatigue monitoring system for automatic understanding and charactering of driver’s conditions, a novel, efficient feature extraction approach, named Local Multiresolution Derivative Pattern (LMDP), is proposed to describe the driver’s fatigue expression images, and the Intersection Kernel Support Vector Machines classifier is then exploited to recognize three pre-defined classes of fatigue expressions, i.e., awake expressions, moderate fatigue expressions and severe fatigue expressions. With features extracted from a fatigue expressions dataset created at Southeast University, the holdout and cross-validation experiments on fatigue expressions classification are conducted by the Intersection Kernel Support Vector Machines classifier, compared with three commonly used classification methods including the k-nearest neighbor classifier, the multilayer perception classifier and the dissimilarity-based classifier. The experimental results of holdout and cross-validation showed that LMDP offers the better performance than Local Derivative Pattern, and the second order LMDP exceeds other order LMDP. With the second order LMDP and the Intersection Kernel Support Vector Machines classifier, the classification accuracies of the severe fatigue are over 90% in the holdout and cross-validation experiments, thus demonstrating the effectiveness of the proposed feature extraction method in automatically understanding the driver’s conditions towards the human-centric driver fatigue monitoring system.
Keywords: Fatigue expression, feature extraction, Local Multiresolution Derivative Pattern (LMDP), Local Derivative Pattern (LDP), Human-centric Driver Fatigue Monitoring System (DFMS)
