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Topic Id:

ID topic:	506
Partner Email:	L.J.M.Rothkrantz@tudelft.nl
Project Title:	Automatic Facial Expression Recognition using a Sparse Learning Relevance Vector Machine
Abstract:	In this thesis the focus is on the realization of a fully automatic emotion recognition system. The exploited approach splits the system into four components. Face detection, facial characteristic point extraction, tracking and classification. Face detection is employed by boosting simple rectangle features that give a decent representation of the face. These features also allow the differentiation between a face and a non-face. The boosting algorithm is combined with an Evolutionary Search to reduce the overall search time. Facial characteristic points (FCP) are extracted from the detected faces. The same technique applied on faces is utilized for this purpose. Additionally, FCP extraction using brightness distribution has also been considered. Finally, after retrieving the required FCPs the emotion of the facial expression can be determined. The Relevance Vector Machine (RVM) is the classification method that is used where a classifier is required.
Advisor:	Leon Rothkrantz
Link:
Degree:	Master
Keywords:
Computer Software
Algorithms & problem solving
Artificial intelligence & Neural networks
Data mining
Data modeling
Data types & Data structures