Bandit framework for systematic learning in wireless video-based face recognition

In most video-based object or face recognition services on mobile devices, each device captures and transmits video frames over wireless to a remote computing service (a.k.a. “cloud”) that performs the heavy-duty video feature extraction and recognition tasks for a large number of mobile devices. The major challenges of such scenarios stem from the highly-varying contention levels in the wireless local area network (WLAN), as well as the variation in the task-scheduling congestion in the cloud. In order for each device to maximize its object or face recognition rate under such contention and congestion variability, we propose a systematic learning framework based on multi-armed bandits. Unlike well-known reinforcement learning techniques that exhibit very slow convergence rates when operating in highly-dynamic environments, the proposed bandit-based systematic learning quickly approaches the optimal transmission and processing-complexity policies based on feedback on the experienced dynamics (contention and congestion levels). Comparisons against state-of-the-art reinforcement learning methods demonstrate that this makes our proposal especially suitable for the highly-dynamic levels of wireless contention and cloud scheduling congestion.