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In this paper, we propose a novel adaptive deep disturbance-disentangled learning (ADDL) method for effective facial expression recognition (FER). ADDL involves a two-stage learning procedure. First, a disturbance feature extraction model (DFEM) is trained to identify multiple disturbing factors on a large-scale face database involving disturbance label information. Second, an adaptive disturbance-disentangled model (ADDM), which contains a global shared subnetwork and two task-specific subnetworks, is designed and learned to explicitly disentangle disturbing factors from facial expression images. In particular, the expression subnetwork leverages a multi-level attention mechanism to extract expression-specific features, while the disturbance subnetwork embraces a new adaptive

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