Crowd video analysis has applications in crowd management, public space design, and visual surveillance. Example tasks potentially aided by automated analysis include anomaly detection (such as a person walking against the grain of traffic or rapid assembly/dispersion of groups of people), population and density measurements, and interactions between groups of people. This survey explores crowd analysis as it relates to two primary research areas: crowd statistics and behavior understanding. First, we survey methods for counting individuals and approximating the density of the crowd. Second, we showcase research efforts on behavior understanding as related to crowds. These works focus on identifying groups, interactions within small groups, and abnormal activity detection such as riots and bottlenecks in large crowds. Works presented in this section also focus on tracking groups of individuals, either as a single entity or a subset of individuals within the frame of reference. Finally, a summary of datasets available for crowd activity video research is provided.

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Biometric identification systems must be able to distin guish between individuals even in situations where the bio metric signature may be similar, such as in the case of iden tical twins. This paper presents experiments done in facial recognition using data from a set of images of twins. This work establishes the current state of facial recognition in regards to twins and the accuracy of current state-of-the art programs in distinguishing between identical twins us ing three commercial face matchers, Cognitec 8.3.2.0, Ver iLook 4.0, and PittPatt 4.2.1 and a baseline matcher em ploying Local Region PCA. Overall, it was observed that Cognitec had the best performance. All matchers, how ever, saw degradation in performance compared to an ex periment where the ability to distinguish unrelated persons was assessed. In particular, lighting and expression seemed to have affected performance the most.

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