Dynamic Human Fatigue Detection Using Feature-Level Fusion

Authors:
Xiao Fan;Bao-Cai Yin;Yan-Feng Sun
Affiliations:
Beijing Key Laboratory of Multimedia and Intelligent Software, College of Computer Science and Technology, Beijing University of Technology, Beijing, China 100022;Beijing Key Laboratory of Multimedia and Intelligent Software, College of Computer Science and Technology, Beijing University of Technology, Beijing, China 100022;Beijing Key Laboratory of Multimedia and Intelligent Software, College of Computer Science and Technology, Beijing University of Technology, Beijing, China 100022
Venue:
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Year:
2008

Citing 4
Cited 0

Face Recognition by Elastic Bunch Graph Matching

IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

IEEE Transactions on Pattern Analysis and Machine Intelligence
A probabilistic framework for modeling and real-time monitoring human fatigue

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantified Score

Hi-index	0.01

Visualization

Abstract

Driver fatigue is a significant factor in many traffic accidents. We propose a novel dynamic features using feature-level fusion for driver fatigue detection from facial image sequences. First, Gabor filters are employed to extract multi-scale and multi-orientation features from each image, which are then merged according to a fusion rule to produce a single feature. To account for the temporal aspect of human fatigue, the fused image sequence is divided into dynamic units, and a histogram of each dynamic unit is computed and concatenated as dynamic features. Finally a statistical learning algorithm is applied to extract the most discriminative features and construct a strong classifier for fatigue detection. The test data contains 600 image sequences from thirty people. Experimental results show the validity of the proposed approach, and the correct rate is much better than the baselines.