Dynamic Face Fatigue Detection Based on Feature-Lever Fusion

Driver fatigue detection is an important research content in the field of HCI (Human-Computer Interaction) and CV (Computer Vision). For the effectiveness about the face image expression using Gabor feature, many researchers introduced the Gabor wavelet to the recognition of fatigue. The dimension of Gabor feature, however, is too high, besides the computation and memory requirements are too large, so it needs dimensional reduction. In this paper, we present a dynamic Gabor feature fatigue analysis method based on features fusion. First, Gabor filters are employed to extract multi-scale and multi-orientation features from each image in the face image sequences. Then we threshold Gabor features and fused them based the encoding manner. Finally, dynamic face features that after fused are extracted for fatigue detection. The method in this paper is able to reduce the dimension of features effectively, and also acquire the satisfactory experimental results.