Genetic syndromes often involve craniofacial malformations. We have investigated whether a computer can recognize disease-specific facial patterns in unrelated individuals. For this, 55 photographs (256 Â 256 pixel) of patients with mucopolysaccharidosis type III (n ¼ 6), Cornelia de Lange (n ¼ 12), fragile X (n ¼ 12), Prader -Willi (n ¼ 12), and Williams-Beuren (n ¼ 13) syndromes were preprocessed by a Gabor wavelet transformation. By comparing the feature vectors at 32 facial nodes, 42/55 (76%) of the patients were correctly classified. In another four patients (7%), the correct and an incorrect diagnosis scored equally well. Clinical geneticists who were shown the same photographs achieved a recognition rate of 62%. Our results prove that certain syndromes are associated with a specific facial pattern and that this pattern can be described in mathematical terms.

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This paper presents a thorough introduction to the real time video surveillance system which has been developed at Bosch Corporate Research considering robustness as the major design goal. A robust surveillance system should especially aim for a low number offalse positives since surveillance guards might get distracted by too many alarms caused by, e.g., moving trees, rain, small camera motion, or varying illumination conditions. Since a missed security related event could cause a serious threat for an installation site, the before mentioned criterion is obviously not suf cient for designing a robust system and thus a low number offalse negatives should simultaneously be achieved. Due to the fact that the false negative rate should ideally be equal to zero, the surveillance system should be able to cope with varying illumination conditions, low contrast and occlusion situations. Besides presenting the building blocks of our video surveillance system, the measures taken to achieve robustness will be illustrated in this paper Since our system is based on algorithms for video motion detection, which has been described e.g. in [1], the previous set of algorithms had to be extended to feature a complete video content analysis system. This transition from simple motion detection to video content analysis is also discussed in the following. In order to measure the performance of our system, quality measures calculated for various PETS sequences will be presented.

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