Information Component Analysis for Perceptual Data

As their prominent characteristics, perceptual data often convey the mixing information, which often results in the inadequate performance for a specific perceptual information processing task due to the interference of irrelevant information components. For example, facial images typically convey the mixing information including identity and expressions. For specific tasks like face and facial expression recognition, the mixing information components are hardly separable, which results in difficulties in either of two tasks. The same problem also exists in speech information processing where speech conveys the mixing information including linguistic, speaker, emotional and environmental characteristics. Furthermore, there is no equal amount of information for mixing components; e.g. linguistics often overwhelmingly dominates the information in speech. The nature of perceptual data gives rise to considerable challenges in their modelling, analysis and recognition. The project is going to investigate and develop a generic approach to information component analysis for perceptual data with state-of-the-art machine learning techniques, e.g., deep architectures. The main issues to be studied include objective-driven high level abstraction of perceptual data in flexible representation forms and novel learning models including architectures and learning algorithms to carry out an information component “filter” and theoretic information aspects in measuring the extracted information components. For demonstration, an information component analysis prototype would be developed for a real application, e.g., information component analysis on facial images. In order take this project, it is essential to have good background knowledge in both machine learning and image/speech signal processing as well as excellent programming skills.