自适应人脸多区域分析的视频心率检测
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安徽高校协同创新项目(GXXT-2019-003); 合肥工业大学“智能互联系统安徽省实验室”开放基金(PA2021AKSK0111)


Video Heart Rate Detection with Adaptive Face Multi-region Analysis
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    摘要:

    心率是衡量人体心血管健康状况和情绪压力的重要生理参数. 然而, 基于视频的非接触式心率检测技术在真实场景中, 会由于人脸运动和光照变化等导致检测准确性的降低. 为了解决上述问题, 考虑到心率检测算法中感兴趣区域(region of interest, ROI)的选取与检测准确度高度相关. 故提出一种自适应超像素分割多区域综合分析的心率检测新方法. 首先利用人脸检测和追踪算法, 裁切获得人脸图像; 之后采用自适应超像素分割算法将ROI划分成互不重叠的子块; 再通过色度特征提取构建各子块原始血液容积脉搏矩阵; 最后对脉搏矩阵使用多指标综合分析并挑选出最佳区域进行心率估计. 实验结果表明, 通过自适应超像素分割和多区域分析优选可以有效提升心率检测准确性. 在静止状态下和运动干扰条件下准确性分别达到99.1%和95.6%, 光照干扰条件下准确性相对传统方法最高提升8.2%. 增强了真实场景下心率检测的鲁棒性.

    Abstract:

    Heart rate is an important physiological parameter for measuring human cardiovascular health and emotional stress. However, video-based non-contact heart rate detection techniques can degrade the detection accuracy in real scenarios due to facial movements and lighting changes. To solve the problem, this study proposes a new method of heart rate detection based on adaptive superpixel segmentation and multi-region integrated analysis depending on the high correlation between the selection of the region of interest (ROI) in a heart rate detection algorithm and its detection accuracy. Firstly, a face detection and tracking algorithm is used to crop the face image. Then the ROI is divided into non-overlapping sub-blocks by an adaptive superpixel segmentation algorithm. The original blood volume pulse matrix of each sub-block is constructed by chromaticity feature extraction. Finally, the pulse matrix is analyzed using multiple indicators, and the best region is selected for heart rate estimation. The experimental results show that the heart rate detection accuracy can be effectively improved by adaptive superpixel segmentation and optimal selection through multi-region analysis. The accuracy reaches 99.1% and 95.6% under stationary and motion disturbance conditions, respectively, and the accuracy is improved by up to 8.2% under illumination disturbance conditions compared with that of the traditional method. The proposed method enhances the robustness of heart rate detection in real scenarios.

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陈宇,杨学志,方帅,李龙伟,王定良.自适应人脸多区域分析的视频心率检测.计算机系统应用,2022,31(10):175-183

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  • 收稿日期:2021-12-31
  • 最后修改日期:2022-01-29
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  • 在线发布日期: 2022-06-28
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