  Realtime face identification (FID) from a video feed is highly
computation-intensive, and may exhaust computation resources if performed on a
device with a limited amount of resources (e.g., a mobile device). In general,
FID performs better when images are sampled at a higher rate, minimizing false
negatives. However, performing it at an overwhelmingly high rate exposes the
system to the risk of a queue overflow that hampers the system's reliability.
This paper proposes a novel, queue-aware FID framework that adapts the sampling
rate to maximize the FID performance while avoiding a queue overflow by
implementing the Lyapunov optimization. A preliminary evaluation via a
trace-based simulation confirms the effectiveness of the framework.
