Bandit Framework for Systematic Learning in Wireless Video-Based Face Recognition

Abstract

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 taskscheduling 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.

Deep Learning-Constructed Joint Transmission-Recognition for Internet of Things

“…On the other hand, there have been a lot of research on image/video transmission over wireless networks with joint design of compression and channel coding, but their target is to restore images/videos instead of performing recognition. Note that for recognition involving images or videos, researches have focused more on compression, without considering joint design with channel coding [13]- [15]. This is due to the difficulty of considering all design factors simultaneously.…”

Untitled

“…Onur Atan et al [21] amazingly launched an innovative Video-based object or face detection services on mobile devices which attracted zooming enthusiasm, in view of the fact that video cameras were then everpresent in the entire mobile communication tools. In a strikingly distinctive environment for the related services, each mobile device captured and communicated video frames over wireless to a far-flung computing cluster (a.k.a.…”