Frontal-view gait recognition by intra- and inter-frame rectangle size distribution

Current trends seem to accredit gait as a sensible biometric feature for human identification, at least in a
multimodal system. In addition to being a robust feature, gait is hard to fake and requires no cooperation
from the user. As in many video systems, the recognition confidence relies on the angle of view of the
camera and on the illumination conditions, inducing a sensitivity to operational conditions that one
may wish to lower.
In this paper we present an efficient approach capable of recognizing people in frontal-view video
sequences. The approach uses an intra-frame description of silhouettes which consists of a set of rectangles
that will fit into any closed silhouette. A dynamic, inter-frame, dimension is then added by aggregating
the size distributions of these rectangles over multiple successive frames. For each new frame, the
inter-frame gait signature is updated and used to estimate the identity of the person detected in the
scene. Finally, in order to smooth the decision on the identity, a majority vote is applied to previous
results. In the final part of this article, we provide experimental results and discuss the accuracy of the
classification for our own database of 21 known persons, and for a public database of 25 persons.