Distributed face recognition using collaborative judgement aggregation in a swarm of tiny wireless sensor nodes

We describe a distributed face recognition strategy that is implemented across a set of independent wireless sensor nodes which have no shared storage. Each node contains a camera of limited aperture, whose images are matched against a randomly selected feature of each face in a statically defined training set. The training set is represented in a distributed in-network storage device that emerges based on a shared protocol running on the sensor nodes. Nodes communicate their findings to other nodes in their immediate vicinity, and eventually the network aggregates these findings to conclude on the identity of the observed target. The independence of the recognition processes on nodes gives rise to the possibility that conflicting results may arise. We compare two consensus building strategies that we have designed to allow the entire network to arrive at a consistent conclusion using efficient communications. Our consensus strategies are based respectively on an optimization of gossiping, and on a distributed feature aggregation process that allows the recognition confidence on each node to monotonically increase.