Publication: Facial features fitting using active appearance model (aam) for driver’s vigilance monitoring
Date
2010-04-01
Authors
Yew, Chuu Tian
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Abstract
This project is about the development of a system to detect the state of facial features based on Active Appearance Model (AAM) that is fast enough to be implemented in real-time processing to detect the driver’s vigilance. To detect the driver’s face in dark or dim lighting condition, we can use infrared camera, which takes image sequence in grayscale video. However, since the
current AAM can be applied in grayscale and even color images using normal cameras, this project concentrates only on this type of camera. To use AAM, we need to construct the AAM by
hand-marking a number of images, then train it by applying Principal Component Analysis to the shape, texture, and the combined model to reduce the number of parameters. After the model
has been built, it is fitted to the face of the input image. To fit it onto the face of input image, AdaBoost face detection algorithm is implemented to detect the face region. Subsequently, AAM
iterative model refinement is applied to the model. After fitting the model to the face, the facial features state is recognized by calculating the difference between the fitted features coordinate
and the shape model data.