Remote physiological measurements, e.g., remote photoplethysmography (rPPG)
based heart rate (HR), heart rate variability (HRV) and respiration frequency
(RF) measuring, are playing more and more important roles under the application
scenarios where contact measurement is inconvenient or impossible. Since the
amplitude of the physiological signals is very small, they can be easily
affected by head movements, lighting conditions, and sensor diversities. To
address these challenges, we propose a cross-verified feature disentangling
strategy to disentangle the physiological features with non-physiological
representations, and then use the distilled physiological features for robust
multi-task physiological measurements. We first transform the input face videos
into a multi-scale spatial-temporal map (MSTmap), which can suppress the
irrelevant background and noise features while retaining most of the temporal
characteristics of the periodic physiological signals. Then we take pairwise
MSTmaps as inputs to an autoencoder architecture with two encoders (one for
physiological signals and the other for non-physiological information) and use
a cross-verified scheme to obtain physiological features disentangled with the
non-physiological features. The disentangled features are finally used for the
joint prediction of multiple physiological signals like average HR values and
rPPG signals. Comprehensive experiments on different large-scale public
datasets of multiple physiological measurement tasks as well as the
cross-database testing demonstrate the robustness of our approach.

本论文提出了一种交叉验证的特征分离策略，以获得与非生理特征分离的生理特征，并使用提取出的生理特征进行多任务生理测量，该方法在多个公共数据集上进行了综合实验，表现出了较好的鲁棒性。

基于视频的远程生理测量：交叉验证的特征分离

Video-based Remote Physiological Measurement via Cross-verified Feature  Disentangling

We propose a novel method for real-time face alignment in videos based on a
recurrent encoder-decoder network model. Our proposed model predicts 2D facial
point heat maps regularized by both detection and regression loss, while
uniquely exploiting recurrent learning at both spatial and temporal dimensions.
At the spatial level, we add a feedback loop connection between the combined
output response map and the input, in order to enable iterative coarse-to-fine
face alignment using a single network model, instead of relying on traditional
cascaded model ensembles. At the temporal level, we first decouple the features
in the bottleneck of the network into temporal-variant factors, such as pose
and expression, and temporal-invariant factors, such as identity information.
Temporal recurrent learning is then applied to the decoupled temporal-variant
features. We show that such feature disentangling yields better generalization
and significantly more accurate results at test time. We perform a
comprehensive experimental analysis, showing the importance of each component
of our proposed model, as well as superior results over the state of the art
and several variations of our method in standard datasets.

本论文提出了一种基于循环编码器 - 解码器网络模型的实时视频人脸对齐方法，该方法预测由检测和回归损失正则化的 2D 人脸关键点热力图，同时在空间和时间维度上独特地利用循环学习。在空间上，我们通过输出响应图和输入之间的反馈环连接，使单个网络模型实现迭代粗略到精细的人脸对齐，而不是依赖于传统的级联模型集成。在时间上，我们首先将网络瓶颈中的特征解耦为时变因素和时不变因素，例如姿态和表情和身份信息。然后应用于解耦的时变特征上的时态循环学习。我们展示了这种特征解耦产生更好的泛化效果，在标准数据集上优于现有技术和本方法的几种变体的全面实验分析。