Abstract
The need for automatic visual surveillance is increasing and the research on person recognition systems is more and more supported. As many biometric recognition methods, e.g. face recognition, are based on quite high camera resolutions which are not available in many situations, we examine features as well as classifier techniques for full body recognition. We present our experiments with color and texture features in the application of full body person recognition. On a database of 53 individuals we tested approved features for object recognition as well as MPEG7 color and texture descriptors on a person recognition task. For comparison, we used an RBF network classifier as well as a nearest-neighbor classifier. Our experiments showed that color as well as texture information is important for a person recognition system. Additionally, a combination of these two kind of features results in a performance improvement.
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