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This paper aims at two problems in fatigue expression recognition. First, texture features extracted by LBP (Local Binary Patterns) descriptor are limited and can not effectively describe the edge and direction information of image. Second, structural features extracted by HOG (Histogram of Oriented Gradient) descriptor are redundant and its computational complexity is high. To fill the gaps of these two problems, we proposed a reconstructed LBPHOG (LBP-RHOG) algorithm which extracted texture spectrum features and edge features from LBP operator and reconstructed HOG operator respectively and obtain fusion information by fusing these two features. To better evaluate the recognition performance, we complete simulation under a self-built fatigue expression database. The results show that our method has low computational complexity and high recognition rate, and can identify fatigue state well.

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