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.
['Xiao Fan', 'Baocai Yin', 'Yanfeng Sun']
Dynamic Human Fatigue Detection Using Feature-Level Fusion