We conduct a formal study of the space of data acquirable from such a device and symbiotic interfaces for its acquisition. This study first compares the datasets in standard face databases to our face database captured from a wearable computer in an everyday environment. Performance of traditional methods on standardized databases is compared to performance on our wearable dataset. Traditional face detection algorithms require a considerable amount of processor and bus bandwidth, which are both correlated with power consumption. Involving the user in the detection process can both improve detection ROC curves and reduce computation prior to recognition.
Effective symbiosis comes at the price of user attentional bandwidth.
Classic face detection and face recognition datasets are constructed with the goal of understanding how to separate faces from their background and how to separate identity across face images respectively. Most of these databases were collected under an experimenter controlled set of camera parameters, lightings, and orientations. It is not immediately clear that results derived from previous experiments will be applicable across the environments where everyday human interaction occurs. Our dataset will help define the problem for wearable contexts. A realistic, useful, system will be able to function in environments like subways, hotel lobbys, malls, and homes at any hour of the day.