Abstract
In the realm of facial analysis, accurate landmark detection is crucial for various applications, ranging from face recognition and expression analysis to animation. Conventional heatmap or coordinate regression-based techniques, however, often face challenges in terms of computational burden and quantization errors. To address these issues, we present the KeyPoint Positioning System (KeyPosS), a groundbreaking facial landmark detection framework that stands out from existing methods. For the first time, KeyPosS employs the True-range Multilateration algorithm, a technique originally used in GPS systems, to achieve rapid and precise facial landmark detection without relying on computationally intensive regression approaches. The framework utilizes a fully convolutional network to predict a distance map, which computes the distance between a Point of Interest (POI) and multiple anchor points. These anchor points are ingeniously harnessed to triangulate the POI's position through the True-range Multilateration algorithm. Notably, the plug-and-play nature of KeyPosS enables seamless integration into any decoding stage, ensuring a versatile and adaptable solution. We conducted a thorough evaluation of KeyPosS's performance by benchmarking it against state-of-the-art models on four different datasets. The results show that KeyPosS substantially outperforms leading methods in low-resolution settings while requiring a minimal time overhead. The code is available at this https URL.
Abstract (translated)
在面部分析领域，准确的地标检测对于各种应用至关重要，包括人脸识别和表情分析到动画。然而，传统的热图或坐标回归based技术通常面临着计算负担和量化错误方面的挑战。为了解决这些问题，我们提出了 KeyPoint Positioning System (KeyPosS)，一个突破性的面部地标检测框架，与现有方法区别开来。首次，KeyPosS采用了True-range Multilateration算法，这是一种最初用于GPS系统的技术，以快速和准确地检测面部地标，而无需依赖计算密集型回归方法。框架使用一个完整的卷积神经网络预测距离地图，该地图计算一个兴趣点(POI)与多个基准点之间的距离。这些基准点通过True-range Multilateration算法巧妙地 harness 起来，以三角化POI的位置。值得注意的是，KeyPosS的可插拔性质使其能够无缝融入任何解码阶段，以确保一个多功能且可适应的解决方案。我们进行了 thorough 评估 KeyPosS 的性能，基准它与传统方法在四个不同数据集上的差异。结果表明，KeyPosS在低分辨率设置下显著优于领先方法，而仅需要最小时间 overhead。代码在此 https URL 可用。
URL
https://arxiv.org/abs/2305.16437
