Recognition of facial expressions - Knowledge Based Systems ...
Recognition of facial expressions - Knowledge Based Systems ...
Recognition of facial expressions - Knowledge Based Systems ...
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TESTING AND RESULTS<br />
The following steps have been taken into account for training the models for the <strong>facial</strong><br />
expression recognition system:<br />
- Obtaining the (Cohn-Kanade) database for building the system’s knowledge<br />
- Conversion <strong>of</strong> data base images from ‘png’ to ‘bmp’ format, 24 bits/pixel<br />
- Increasing the quality <strong>of</strong> the images through some enhancement procedures (light,<br />
removing strips, applying filters, etc.)<br />
- Extracting some Facial Characteristic Points (FCPs) by using a special tool (FCP<br />
Management Application)<br />
- Computing the value <strong>of</strong> some parameters according to a given model. Applying a<br />
discretization procedure by using a special application (Parameter Discretization<br />
Application)<br />
- Determining the <strong>facial</strong> expression for each <strong>of</strong> the samples in the database by<br />
analyzing the sequence <strong>of</strong> Action Units (AUs). The tool used to process the files<br />
in the database was Facial Expression Assignement Application.<br />
- Using different kind <strong>of</strong> reasoning mechanisms for emotion recognition. The<br />
training step took into account the data provided from the previous steps.<br />
Bayesian Belief Networks (BBN) and back-propagation Artificial Neuronal<br />
Networks (ANN) were the main modalities for recognition.<br />
- Principal Component Analysis technique was used as an enhancement procedure<br />
for the emotion recognition.<br />
The steps that imply testing the recognition models are:<br />
- Capture the video signal containing the <strong>facial</strong> expression<br />
- Detecting the Facial Characteristic Points automatically<br />
- Computing the value <strong>of</strong> the model parameters<br />
- Using the parameter values for emotion detection