Pain is a personal, subjective experience that is commonly evaluated through
visual analog scales (VAS). While this is often convenient and useful,
automatic pain detection systems can reduce pain score acquisition efforts in
large-scale studies by estimating it directly from the participants' facial
expressions. In this paper, we propose a novel two-stage learning approach for
VAS estimation: first, our algorithm employs Recurrent Neural Networks (RNNs)
to automatically estimate Prkachin and Solomon Pain Intensity (PSPI) levels
from face images. The estimated scores are then fed into the personalized
Hidden Conditional Random Fields (HCRFs), used to estimate the VAS, provided by
each person. Personalization of the model is performed using a newly introduced
facial expressiveness score, unique for each person. To the best of our
knowledge, this is the first approach to automatically estimate VAS from face
images. We show the benefits of the proposed personalized over traditional
non-personalized approach on a benchmark dataset for pain analysis from face
images.

该文章提出了一种基于面部表情的自动疼痛感知系统，使用递归神经网络和隐藏式条件随机场进行自动疼痛强度和视觉模拟评分的估计。通过引入每个人的面部表情得分进行模型的个性化定制，证明了个性化方法优于传统的非个性化方法。

面部表情自动估计个性化自报疼痛强度

Personalized Automatic Estimation of Self-reported Pain Intensity from  Facial Expressions

This paper proposes a new approach to model the temporal dynamics of a
sequence of facial expressions. To this purpose, a sequence of Face Image
Descriptors (FID) is regarded as the output of a Linear Time Invariant (LTI)
system. The temporal dynamics of such sequence of descriptors are represented
by means of a Hankel matrix. The paper presents different strategies to compute
dynamics-based representation of a sequence of FID, and reports classification
accuracy values of the proposed representations within different standard
classification frameworks. The representations have been validated in two very
challenging application domains: emotion recognition and pain detection.
Experiments on two publicly available benchmarks and comparison with
state-of-the-art approaches demonstrate that the dynamics-based FID
representation attains competitive performance when off-the-shelf
classification tools are adopted.

本文提出了一种新方法来建模面部表情序列的时间动态，通过将面部图像描述符（FID）的序列视为线性时不变系统（LTI）的输出以实现这一目的，利用 Hankel 矩阵表示这些描述符的时间动态。该文提出了不同策略来计算基于动态的 FID 序列的表征，并在情感识别和疼痛检测两个挑战性应用领域进行了验证，并在两个公开基准测试场中进行了实验，结果表明，当使用现成的分类工具时，基于动态的 FID 表征具有很高的分类准确率。