Dual-channel feature disentanglement for identity-invariant facial expression recognition

Facial expression recognition is a challenging task owing to subtle inter-class differences and significant intra-class variations. To address this problem, we propose a novel dual-channel alternation training strategy, in which image pairs with different expressions from the same identity and image pairs with the same expression from different identities are alternately fed into a Siamese