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Automatic Emotion Detection, Analysis and Recognition

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  • Full or part time
    Dr K Chen
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

Affective computing is a branch of Artificial Intelligence that relates to, arises from, or deliberately influences emotion and other affective phenomena. Research in affective computing is of interdisciplinary nature, which combines computer science with many other fields, e.g., psychology, cognitive science, neuroscience, sociology, medicine, psychophysiology, ethics, and philosophy, in order to enable advances in basic understanding of affect and its role in biological agents, and across a broad range of human experience. From a human-machine interaction perspective, the most important topic in affective computing is automatic emotion detection, analysis and recognition from human behaviours including facial expression, speech and body gestures.

In general, this project is going to investigate critical problems underlying emotional representation learning, emotional pattern discovery, emotional pattern modelling and recognition. This is a flexible project; i.e., it could be either a fundamental research oriented project that learns a ’universal’ emotion representation that is insensitive to different factors or a practical project that applies state-of-the-art machine learning and signal processing techniques to the emotion detection and recognition in a real scenario. In addition, this project mainly focuses on mono-modal emotion but can also be extended to the development of multimodal affective computing techniques, i.e., fusion of different emotional information for decision making. For demonstration, a prototype normally needs to be established based on the proposed approaches for a real application, e.g., computerized tutoring in an e-learning environment. While the relevant fundamental research is expected to be conducted, the project is suitable for one who has a clear targeted application area in mind.

In order to take this project, it is essential to have good machine learning, speech/image signal processing and Psychological background knowledge on emotion theories (if working on fundamental research) as well as excellent programming skills (if working on applications). If you are interested in this project, please first visit my research student page: http://staff.cs.manchester.ac.uk/~kechen/ for the required materials and information prior to contacting me.

Funding Notes

Candidates who have been offered a place for PhD study in the School of Computer Science may be considered for funding by the School. Each year around 20 new PhD students are awarded funding via the School. Further details on School funding can be found at: http://www.cs.manchester.ac.uk/study/postgraduate-research/programmes/phd/funding/school-studentships/.

In addition, exceptional students may be considered for the President's Doctoral Scholar Award and the Dean's Award. Further details on these opportunities can be found at: http://www.eps.manchester.ac.uk/our-research/funding/.



For further details, please see our funding pages here: http://www.cs.manchester.ac.uk/study/postgraduate-research/programmes/phd/funding/
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References

Supervisor's Webpage: http://www.cs.man.ac.uk/~kechen/.

How good is research at University of Manchester in Computer Science and Informatics?

FTE Category A staff submitted: 44.86

Research output data provided by the Research Excellence Framework (REF)

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