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.

翻译：在人脸关键点检测（FLD）中实现精度与效率的平衡是一项关键挑战。本文提出并行最优位置搜索（POPoS），这是一种高精度编码-解码框架，旨在解决传统FLD方法的局限性。POPoS包含三项核心贡献：（1）采用伪距多点定位校正热图误差，提升关键点定位精度。通过整合多个锚点，该方法降低了个别热图不准确性的影响，从而实现鲁棒的整体定位。（2）为提高所选锚点的伪距精度，提出了一种名为多点定位锚点损失的新损失函数。该损失函数提升了距离图的准确性，缓解了陷入局部最优的风险，并确保获得最优解。（3）引入单步并行计算算法，显著提升计算效率并减少处理时间。在五个基准数据集上的广泛评估表明，POPoS始终优于现有方法，尤其在低分辨率热图场景下表现突出，且计算开销极小。这些优势使POPoS成为FLD领域一种高效、精确的工具，在实际应用场景中具有广泛的适用性。