School of Electronic Engineering and Computer Science

Machine Learning for Computer Vision and Affective Computing

Supervisor: Prof Ioannis Patras

Candidates that are interested in the following two topics, or in closely related areas in Computer Vision and Affective Computing, are encouraged to contact Ioannis Patras ( directly via email with a copy of their CV.

Project 1) Deep Neural Networks for Affective Computing.
This project aims at developing Machine Learning methodologies for analysing Human Signals for analysis and recognition of affective, cognitive and mental states. The emphasis will be on Deep Learning methodologies for analysis of neurophysiological signals such as EEG and physiological signals such EDA and ECG and applications in interaction with multimedia content, building on the works of Patras, Koelstra and Correa (,

Project 2) Deep Neural Networks for Human Behaviour Analysis
This project aims at Machine Learning methodologies for analysis at various levels of human behaviour, e.g. recognition of human actions, affective states and the interactions between humans and their environment. At a lower level this involves modeling and recognition of objects, facial expressions, body poses/gestures and actions. A particular focus will be on Deep Learning architectures and on especially on ways of transferring knowledge between the different tasks.

Please, look at related publications at:

For further information, please email directly, attaching a copy of your CV.