Enabling Sustainable and Unmanned Facial Detection and Recognition Services With Adaptive Edge Resource

Facial recognition techniques are used extensively in areas like online payments, education, and social media. Traditionally, these applications relied on powerful cloud-based systems, but advancements in edge computing have changed this, enabling fast and reliable local processing in complex and extreme environments. However, new challenges arise in availability and durability insurance to make the system run 24/7 with acceptable performance. This paper proposes a novel solution to these challenging settings. First, we use edge devices for local data processing, reducing the need for cloud communication and enhancing user privacy. Second, we implement an adaptive control strategy to improve energy management in these devices. Lastly, we establish a solar-powered energy system to facilitate long-term device operation. The experiments show our approach strikes a balance between performance, quality, and durability, enabling facial recognition systems to work energy-efficiently in complex environments. Meanwhile, considering the limited resources of devices in extreme cases, we also proposed a learning-based approach to accelerate the solution generation.