提出并行最优位置搜索方法，提升人脸关键点检测精度与效率。
POPoS: Improving Efficient and Robust Facial Landmark Detection with Parallel Optimal Position Search
- 通过伪距离多边定位修正热图误差，提升定位鲁棒性。
- 引入多边定位锚点损失函数，优化距离图精度并避免局部最优。
- 单步并行计算加速处理，低分辨率下表现尤为出色。
在人脸关键点检测（FLD）中，精度与效率的平衡是一大挑战。本文提出并行最优位置搜索（POPoS），一种高精度编码-解码框架，旨在解决传统方法的局限性。主要贡献包括：(1) 采用伪距离多边定位修正热图误差，通过集成多个锚点降低单个热图不准确的影响，提升整体定位鲁棒性；(2) 提出新型多边定位锚点损失函数，增强距离图精度，缓解局部最优风险，确保最优解；(3) 设计单步并行计算算法，显著提升计算效率，减少处理时间。在五个基准数据集上的广泛评估表明，POPoS持续优于现有方法，尤其在低分辨率热图场景中表现优异，且计算开销极小。这些优势使其成为实际应用中高效精准的人脸关键点检测工具。
原文摘要 · Abstract (English)
Achieving a balance between accuracy and efficiency is a critical challenge in facial landmark detection (FLD). This paper introduces Parallel Optimal Position Search (POPoS), a high-precision encoding-decoding framework designed to address the limitations of traditional FLD methods. POPoS employs three key contributions: (1) Pseudo-range multilateration is utilized to correct heatmap errors, improving landmark localization accuracy. By integrating multiple anchor points, it reduces the impact of individual heatmap inaccuracies, leading to robust overall positioning. (2) To enhance the pseudo-range accuracy of selected anchor points, a new loss function, named multilateration anchor loss, is proposed. This loss function enhances the accuracy of the distance map, mitigates the risk of local optima, and ensures optimal solutions. (3) A single-step parallel computation algorithm is introduced, boosting computational efficiency and reducing processing time. Extensive evaluations across five benchmark datasets demonstrate that POPoS consistently outperforms existing methods, particularly excelling in low-resolution heatmaps scenarios with minimal computational overhead. These advantages make POPoS a highly efficient and accurate tool for FLD, with broad applicability in real-world scenarios.
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