By D. E. Rutherford
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2004; Yin et al. 2004). Example images of different facial expressions are shown in Fig. 1. Although a facial expression is a dynamic process with important information captured in motion (Bassili 1979), imagery features extracted from static face images are often used to represent a facial expression. These features include both geometric features (Valstar and Pantic 2006; Valstar et al. 2005) and appearance features (Bartlett et al. 2005; Donato et al. 1999; Lyons et al. 1999; Tian 2004; Zhang et al.
Solving this problem has compelling practical applications, such as monitoring long-term activity patterns of targeted individuals over large spaces and across time zones. 7 Distributed Behaviour 27 Fig. 11 An illustration of different degrees of overlap between the field of view of five cameras: (a) cameras partially overlap with adjacent cameras, (b) all cameras partially overlap with each other, (c) non-overlapping camera network, (d) the most common case involving different types of overlapping.
Intell. : Action detection in complex scenes with spatial and temporal ambiguities. In: IEEE International Conference on Computer Vision, Kyoto, Japan, October 2009, pp. : Tracking across multiple cameras with disjoint views. In: IEEE International Conference on Computer Vision, pp. : Appearance modeling for tracking in multiple nonoverlapping cameras. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. : Multi-resolution patch tensor for facial expression hallucination. In: IEEE Conference on Computer Vision and Pattern Recognition, New York, USA, June 2006, pp.