Difference between revisions of "Online Emotion Classification"
(New page: == '''Project profile''' == === Project name === Online Emotion Classification === Project short description === The project focuses on the development of a software framework for onl...) |
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The framework for online emotion classification will be composed of: | The framework for online emotion classification will be composed of: | ||
| − | + | * A client application that receives data (XML data) from some generic source (A server application that polls sensors and generate the XML data with the sampled biometric signals). [C++/libXML/MatlabAPI] | |
| − | + | * An online classifier that can be feed with the data received by the client and outputs the result of the classification. [Matlab/Java] | |
| − | + | * An offline trainer to train the classifier with previously collected data. [Matlab/Java] | |
| − | + | * A 3d GUI that can be connected to the online classifier to show the result of the classification. [C++/OGRE] | |
The client parses the received XML and interacts through the Matlab API with the Matlab engine, launching the classifier and passing it the parsed data. | The client parses the received XML and interacts through the Matlab API with the Matlab engine, launching the classifier and passing it the parsed data. | ||
Revision as of 19:02, 10 November 2008
Contents
Project profile
Project name
Online Emotion Classification
Project short description
The project focuses on the development of a software framework for online emotion classification. A general framework will be developed to support emotion detection using several biometric signals.
Dates
Start date: 2008/10/07
End date: -
Internet site(s)
People involved
= Project leaders
Students
Students currently working on the project
Project description ==
The framework for online emotion classification will be composed of:
- A client application that receives data (XML data) from some generic source (A server application that polls sensors and generate the XML data with the sampled biometric signals). [C++/libXML/MatlabAPI]
- An online classifier that can be feed with the data received by the client and outputs the result of the classification. [Matlab/Java]
- An offline trainer to train the classifier with previously collected data. [Matlab/Java]
- A 3d GUI that can be connected to the online classifier to show the result of the classification. [C++/OGRE]
The client parses the received XML and interacts through the Matlab API with the Matlab engine, launching the classifier and passing it the parsed data.
The classifiers are based on the Weka classification engine. After a strong preprocessing on the data done with Matlab, the signals are classified using Weka.
The GUI contains several models of human faces that can change expressions according to the received classification signal from the online classifier.
