A system for automatic pain detection whereby pain-related features are extracted from facial images using a four-layer Convolutional Deep Belief Network (CDBN) is proposed in this study. The CDBN is trained by greedy layer-wise procedure whereby each added layer is trained as a Convolutional Restricted Boltzmann Machine (CRBM) by contrastive divergence. Since conventional CRBM is trained in a purely unsupervised manner, there is no guarantee that learned features are appropriate for the supervised task at hand. A discriminative objective based on between-class and within-class distances is proposed to adapt CRBM to learn task-related features. When discriminative and generative objectives are appropriately combined, a competitive classification performance can be achieved. Moreover, we introduced batch normalization (BN) units in the structure of the CRBM model to smooth optimization landscape and speed up the learning process. BN units come right before sigmoid units. Extracted features are then used to train a linear SVM to classify each frame into pain or no-pain classes. Extensive experiments on UNBC-McMaster Shoulder Pain database demonstrate the effectiveness of the proposed method for automatic pain detection.

在这项研究中提出了一种自动疼痛检测系统，该系统使用四层卷积深度信念网络（CDBN）从面部图像中提取与疼痛相关的特征。通过贪婪的逐层过程训练CDBN，从而通过对比发散将每个添加的层训练为卷积受限玻尔兹曼机（CRBM）。由于常规CRBM的训练完全是无监督的，因此无法保证学习的功能适用于手头的监督任务。提出了一种基于类间距离和类内距离的判别目标，以使CRBM适应学习任务相关特征。当区分性目标和生成性目标适当组合时，可以实现具有竞争力的分类性能。而且，我们在CRBM模型的结构中引入了批量归一化（BN）单元，以平滑优化环境并加快学习过程。BN单位紧接S形单位。然后，将提取的特征用于训练线性SVM，以将每个帧分类为疼痛或无痛类别。在UNBC-McMaster肩痛数据库上进行的大量实验证明了所提出的自动疼痛检测方法的有效性。