Amy Victoria Aragones

Learning person-specific models for facial expression and action unit recognition

A key assumption of traditional machine learning approach is that the test data are draw from the same distribution as the training data. 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. As a result, previous work has shown that learning from adequate person-specific data can improve the expression recognition performance over the one from generic data. However, person-specific data is typically very sparse in real-world applications due to the difficulties of data collection and labeling, and learning from sparse data may suffer from serious over-fitting. In this paper, we propose to learn a person-specific model through transfer learning. By transferring the informative knowledge from other people, it allows us to learn an accurate model for a new subject with only a small amount of person-specific data. We conduct extensive experiments to compare different person-specific models for facial expression and action unit (AU) recognition, and show that transfer learning significantly improves the recognition performance with a small amount of training data.

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.

Towards autonomic systems for lifecycle support of complex equipment

GE has been a leader in remote monitoring and diagnostics of complex systems, such as medical imaging equipment and aircraft engines, for a number of years. The data gathered and the analytical capabilities developed have naturally lead toward service contracts to maintain individual machines and fleets of machines while lowering the cost of ownership for our customers. As information technology extends the reach of diagnostic, prognostic, and decision support systems, opportunities to optimize support of specific organizational objectives is enhanced. Such a decision support system is envisioned and is inspired by the human autonomic nervous system.