Supervised face recognition for railway stations surveillance

Abstract

The feasibility of a supervised surveillance system for railway stations (or airports) is evaluated. Surveillance is based on suspicious recognition by means of video cameras. As the problem involves both face detection and face recognition, we have evaluated the best performing algorithms of these two areas. For face detection, we have selected the Viola-Jones algorithm; while for face recognition we have performed tests with an appearance based algorithm (PCA) and an interest-point based algorithm (SIFT). We have used both the AT&T database and the LFW database for our tests. The results obtained show that face detection works reliably and fast enough, but face recognition cannot cope with highly non-homogeneous images like those of LFW and requires parallel computing in order to work in real time. As a conclusion, supervised surveillance is feasible provided image homogeneity fulfils some minimum standards and parallel computing is used. Besides, interest-point based methods are more robust to image quality, so their use is encouraged. © 2009 Springer Berlin Heidelberg.