Non-contact video-based physiological measurement has many applications in
health care and human-computer interaction. Practical applications require
measurements to be accurate even in the presence of large head rotations. We
propose the first end-to-end system for video-based measurement of heart and
breathing rate using a deep convolutional network. The system features a new
motion representation based on a skin reflection model and a new attention
mechanism using appearance information to guide motion estimation, both of
which enable robust measurement under heterogeneous lighting and major motions.
Our approach significantly outperforms all current state-of-the-art methods on
both RGB and infrared video datasets. Furthermore, it allows spatial-temporal
distributions of physiological signals to be visualized via the attention
mechanism.

本文提出了一种使用深度卷积神经网络进行基于视频的非接触式心率和呼吸率测量的新方法，该系统采用一种基于皮肤反射模型的新动作表示和一种使用外观信息引导运动估计的新注意机制，从而实现在异质光照和主要运动下强大的测量，其空间 - 时域分布可通过注意机制进行可视化，能够显著优于所有当前最先进的 RGB 和红外视频数据集上的方法。