基于面部视频的 rPPG 的 ROI 选择评估,Sensors

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基于面部视频的 rPPG 的 ROI 选择评估
Sensors ( IF 3.4 ) Pub Date : 2021-11-27 , DOI: 10.3390/s21237923
Dae-Yeol Kim _{1,

2} , Kwangkee Lee ₁ , Chae-Bong Sohn ₂

Affiliation

通常，基于面部图像的远程光电容积脉搏波 (rPPG) 方法使用基于颜色和基于补丁的感兴趣区域 (ROI) 选择方法来估计血容量脉冲 (BVP) 和每分钟心跳 (BPM)。从解剖学上讲，面部所有区域的皮肤厚度并不均匀，因此无法在每个区域获得相同的漫反射信息。近年来，各种研究已经提出了其 ROI 的实验结果，但没有为提议的区域提供有效的理由。在本文中，为了查看皮肤厚度对 rPPG 算法准确性的影响，我们对 39 个解剖学划分的面部区域进行了实验。使用七种算法（CHROM、GREEN、ICA、PBV、POS、SSR、和 LGI) 使用 UBFC-rPPG 和 LGI-PPGI 数据集，考虑了 39 个解剖学分类区域中的 29 个选定区域和两个调整区域。我们提出了一个 BVP 相似性评估指标来找到一个高精度的区域。我们对 TOP-5 区域和 BOT-5 区域进行了额外的实验，并展示了建议的 ROI 的有效性。TOP-5 区域与之前算法的 ROI 相比显示出相对较高的精度，表明在开发基于面部图像的 rPPG 算法时应考虑 ROI 的解剖特征。

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ssessment of ROI Selection for Facial Video-Based rPPG

In general, facial image-based remote photoplethysmography (rPPG) methods use color-based and patch-based region-of-interest (ROI) selection methods to estimate the blood volume pulse (BVP) and beats per minute (BPM). Anatomically, the thickness of the skin is not uniform in all areas of the face, so the same diffuse reflection information cannot be obtained in each area. In recent years, various studies have presented experimental results for their ROIs but did not provide a valid rationale for the proposed regions. In this paper, to see the effect of skin thickness on the accuracy of the rPPG algorithm, we conducted an experiment on 39 anatomically divided facial regions. Experiments were performed with seven algorithms (CHROM, GREEN, ICA, PBV, POS, SSR, and LGI) using the UBFC-rPPG and LGI-PPGI datasets considering 29 selected regions and two adjusted regions out of 39 anatomically classified regions. We proposed a BVP similarity evaluation metric to find a region with high accuracy. We conducted additional experiments on the TOP-5 regions and BOT-5 regions and presented the validity of the proposed ROIs. The TOP-5 regions showed relatively high accuracy compared to the previous algorithm’s ROI, suggesting that the anatomical characteristics of the ROI should be considered when developing a facial image-based rPPG algorithm.

更新日期：2021-11-27

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