Multi-Modal Signal Processing

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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].

5.6 CONCLUSIONS

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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