Abstract
Expression recognition has achieved impressive success in recent years. Most methods are based on the assumption that the training and testing databases follow the same feature distribution. However, distribution discrepancy among datasets is pretty common in practical scenarios. Thus, the performance of these methods may drop sharply on target datasets. To address this issue, we aim to learn a facial expression classification model from several labeled source databases and generalize it to target databases. This is achieved by integrating domain alignment and class-compact features learning across source domains. Domain alignment paves the way to involve more expression-related representations. Learning compact features can signicantly diminish the intra-class divergence, which is beneficial to both domain alignment and expression recognition. Experimental results demonstrate that the proposed model has a more promising performance compared with other cross-database expression recognition methods.
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This work was partially financially supported by National Natural Science Foundation of China under grant 61533012 and 91748120.
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Wang, L., Su, J., Zhang, K. (2019). Cross-Database Facial Expression Recognition with Domain Alignment and Compact Feature Learning. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_34
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