Efficient Facial Landmark Detection for Embedded Systems
This addresses the problem of power consumption and latency for facial landmark detection on edge devices, though it appears incremental.
The paper tackles efficient facial landmark detection for embedded systems by introducing the EFLD model with a lightweight backbone and flexible detection head, achieving superior performance in the IEEE ICME 2024 Grand Challenges PAIR Competition.
This paper introduces the Efficient Facial Landmark Detection (EFLD) model, specifically designed for edge devices confronted with the challenges related to power consumption and time latency. EFLD features a lightweight backbone and a flexible detection head, each significantly enhancing operational efficiency on resource-constrained devices. To improve the model's robustness, we propose a cross-format training strategy. This strategy leverages a wide variety of publicly accessible datasets to enhance the model's generalizability and robustness, without increasing inference costs. Our ablation study highlights the significant impact of each component on reducing computational demands, model size, and improving accuracy. EFLD demonstrates superior performance compared to competitors in the IEEE ICME 2024 Grand Challenges PAIR Competition, a contest focused on low-power, efficient, and accurate facial-landmark detection for embedded systems, showcasing its effectiveness in real-world facial landmark detection tasks.