Person-specific expression recognition with transfer learning

A key assumption of traditional machine learning is that both the training and test data share the same distribution. However, this assumption does not hold in many real-world scenarios. For example, in facial expression recognition, the appearance of an expression may vary significantly for different people. Previous work has shown that learning from adequate person-specific data can improve facial expression recognition results. However, because of the difficulties of data collection and labeling, person-specific data is usually very sparse in real-world applications. Learning from the sparse data may suffer from serious over-fitting. In this paper, we propose to learn a person-specific facial expression model through transfer learning. By transferring the informative knowledge from other people, it allows us to learn an accurate person-specific model for a new subject with only a small amount of his/her specific data.