Automatic Emotion Detection, Analysis and Recognition.

Primary supervisor

Additional information

Contact admissions office

Other projects with the same supervisor

Funding

  • Competition Funded Project (Students Worldwide)
This research project is one of a number of projects at this institution. It is in competition for funding with one or more of these projects. Usually the project which receives the best applicant will be awarded the funding. Applications for this project are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full department and project details for further information.

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.

Person specification

For information

Essential

Applicants will be required to evidence the following skills and qualifications.

  • This project requires mathematical engagement and ability substantially greater than for a typical Computer Science PhD. Give evidence for appropriate competence, as relevant to the project description.
  • You must be capable of performing at a very high level.
  • You must have a self-driven interest in uncovering and solving unknown problems and be able to work hard and creatively without constant supervision.

Desirable

Applicants will be required to evidence the following skills and qualifications.

  • You will possess determination (which is often more important than qualifications) although you'll need a good amount of both.
  • You will have good time management.

General

Applicants will be required to address the following.

  • Discuss your final year Undergraduate project work - and if appropriate your MSc project work.
  • How well does your previous study prepare you for undertaking Postgraduate Research?
  • Comment on your transcript/predicted degree marks, outlining both strong and weak points.
  • Why do you believe you are suitable for doing Postgraduate Research?
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