Fusion of Multiple Features for Identity Estimation

In a visual surveillance application where a network of cameras is used to observe many people, there are numerous situations where the consistent identification of a person is an important component in the system. There are many approaches to describing the appearance of a person. In this paper, we examine a combination of two identity descriptors: standard MPEG-7 color descriptors, and spatial descriptors including the height and texture of the object. Using a data set of 47 people with 6 observations per person, we show that the fusion of information given by these descriptors in an information-theoretic framework gives an improvement over using individual descriptors alone. This approach is compared to a metric used in QBE (Query By Example) applications, relating the theory to the practical need in a visual surveillance application to ascertain the identity of a person.