Explaining concepts by contrasting examples is an efficient and convenient way of giving insights into the reasons behind a classification decision. This is of particular interest in decision-critical domains, such as medical diagnostics. One particular challenging use case is to distinguish facial expressions of pain and other states, such as disgust, due to high similarity of manifestation. In this paper, we present an approach for generating contrastive explanations to explain facial expressions of pain and disgust shown in video sequences. We implement and compare two approaches for contrastive explanation generation. The first approach explains a specific pain instance in contrast to the most similar disgust instance(s) based on the occurrence of facial expressions (attributes). The second approach takes into account which temporal relations hold between intervals of facial expressions within a sequence (relations). The input to our explanation generation approach is the output of an interpretable rule-based classifier for pain and disgust.We utilize two different similarity metrics to determine near misses and far misses as contrasting instances. Our results show that near miss explanations are shorter than far miss explanations, independent from the applied similarity metric. The outcome of our evaluation indicates that pain and disgust can be distinguished with the help of temporal relations. We currently plan experiments to evaluate how the explanations help in teaching concepts and how they could be enhanced by further modalities and interaction.

通过对比样例解释概念是一种有效和方便的方式，可以深入了解分类决策背后的原因。本文提出了一种生成对比解释的方法，以解释视频序列中的疼痛和厌恶的面部表情。我们比较了两种生成对比解释的方法，并发现近似解释要比远离解释更短，这与应用的相似性度量无关。通过评估结果表明，通过考虑时间关系可以区分出疼痛和厌恶。我们计划进行实验以评估解释对于教学概念的帮助，并通过其他模式和交互方式进一步增强解释的能力。

运用基于属性和关系的相似案例解释: 一种可解释的区分疼痛和厌恶面部表情的方法