Facial expression recognition suffers under realworld conditions, especially on unseen subjects due to high inter-subject variations. To alleviate variations introduced by personal attributes and achieve better facial expression recognition performance, a novel identity-aware convolutional neural network (IACNN) is proposed. In particular, a CNN with a new architecture is employed as individual streams of a bi-stream identity-aware network. An expression-sensitive contrastive loss is developed to measure the expression similarity to ensure the features learned by the network are invariant to expression variations. More importantly, an identity-sensitive contrastive loss is proposed to learn identity-related information from identity labels to achieve identity-invariant expression recognition. Extensive experiments on three public databases including a spontaneous facial expression database have shown that the proposed IACNN achieves promising results in real world.
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