Multi-scale dynamic human fatigue detection with feature level fusion

Driver fatigue is a significant reason for many traffic accidents. We propose a novel multi-scale dynamic feature with 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. Features of the same scale are then fused 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 AdaBoost 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 average correct rate is 99.33% which is much better than the baselines.