We propose a novel and general framework, named the multithreading cascade of rotation-invariant histograms of oriented gradients (McRiHOG) for facial expression recognition (FER). In this paper, we attempt to solve two problems about high-quality local feature descriptors and robust classifying algorithm for FER. The first solution is that we adopt annular spatial bins type HOG (Histograms of Oriented Gradients) descriptors to describe local patches. In this way, it significantly enhances the descriptors in regard to rotation-invariant ability and feature description accuracy; The second one is that we use a novel multithreading cascade to simultaneously learn multiclass data. Multithreading cascade is implemented through non-interfering boosting channels, which are respectively built to train weak classifiers for each expression. The superiority of McRiHOG over current state-of-the-art methods is clearly demonstrated by evaluation experiments based on three popular public databases, CK+, MMI, and AFEW.
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