PART|I Signal Processing, Modelling and Related Mathematical Tools
the methods used in [86, 87] extract silhouettes and edges in the
images and measure the particle likelihood (fitness) as a linear
combination between the silhouette overlap of the body model
projected onto the images and the distance between the detected
edges and the projected ones from the body model. Some other
approaches to this problem build up a 3D reconstruction of the
scene and generate their measurements directly on this space as
done in [58].
In this chapter, we have reviewed image and video analysis tools used
for HCI. Face and hand analysis techniques that can be used in close
view interfaces, such as desktop computer applications, have been
described.Applications for face and hand analysis include face recog-
nition, facial expression recognition and gaze estimation. Examples
of these applications have also been described. Most of these tech-
niques are restricted not only to close-view frontal faces but also to
predetermined hand poses and gestures.
Multiple camera scenarios allow to develop HCI applications for
more general situations. Head and body pose can be determined more
accurately when using multiple points of view, solving occlusion or
perspective problems. In this context, we have first reviewed the
tools used for head orientation estimation, and how they are used
for FoA estimation applications. Finally, techniques for body gesture
analysis have been described. Although simple features can be used
for recognising some specific actions, more complex analysis using
articulated body models is needed for a complete knowledge of the
human motion.
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