面向嵌入式系统的高效人脸关键点检测
Efficient Facial Landmark Detection for Embedded Systems
AI总结：
针对边缘设备功耗与时延限制，本文提出带轻量骨干与灵活检测头的EFLD模型，结合跨格式训练策略提升鲁棒性，在IEEE ICME 2024 PAIR竞赛中优于同类方法，适配嵌入式人脸关键点检测需求。
摘要：
本文提出了高效人脸关键点检测（Efficient Facial Landmark Detection，EFLD）模型，专为面临功耗与时延挑战的边缘设备设计。EFLD 具备轻量级骨干网络和灵活的检测头，二者均显著提升了模型在资源受限设备上的运行效率。为提升模型鲁棒性，我们提出了一种跨格式训练策略，该策略利用各类公开可用的数据集增强模型的泛化性与鲁棒性，且不会增加推理成本。我们的消融实验明确了各组件在降低计算需求、压缩模型尺寸及提升准确率方面的显著作用。在聚焦嵌入式系统低功耗、高效、精准人脸关键点检测的 IEEE ICME 2024 大挑战赛 PAIR 竞赛中，EFLD 表现优于同类竞品，证明了其在实际人脸关键点检测任务中的有效性。
英文摘要：
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.
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