# A Reliable, Self-Adaptive Face Identification Framework via Lyapunov Optimization

arXiv: https://arxiv.org/abs/2109.01212 · HTML: https://mlsanity.com/paper/2109.01212/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 2 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks](https://mlsanity.com/paper/1902.10932/) (2019)
- [Wireless Video Caching and Dynamic Streaming under Differentiated Quality Requirements](https://mlsanity.com/paper/1806.01594/) (2018)
- [Dynamic Power Allocation and User Scheduling for Power-Efficient and Low-Latency Communications](https://mlsanity.com/paper/1807.00682/) (2018)
- [Joint Distributed Link Scheduling and Power Allocation for Content Delivery in Wireless Caching Networks](https://mlsanity.com/paper/1911.13010/) (2019)

## Abstract (arXiv)

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.