Empathy ability is one of the most important social communication skills in early childhood development. To analyze the children's empathy ability, facial expression analysis (FEA) is an effective way due to its ability to understand children's emotional states. Previous works mainly focus on recognizing the facial expression categories yet fail to estimate expression intensity, the latter of which is more important for fine-grained emotion analysis. To this end, this article first proposes to analyze children's empathy ability with both the categories and the intensities of facial expressions. A novel FEA method based on intensity label distribution learning is presented, which aims to recognize expression categories and estimate their intensity levels in an end-to-end framework. First, the intensity label distribution is generated for each frame in the expression sequence using a linear interpolation estimation and a Gaussian function to address the lack of reasonable annotations for expression intensity. Then, the extended intensity label distribution is presented to automatically encode the expression intensity in a multidimensional expression space, which aims to integrate the expression recognition and intensity estimation into a unified framework as well as boost the expression recognition performance by suppressing the variations in appearance caused by intensity and by emphasizing those variations among weak expressions. Finally, a Siamese-like convolutional neural network is presented to learn the expression model from a pair of frames that includes an expressive frame and its corresponding neutral frame using the extended intensity label distribution as the supervised information, thus effectively eliminating the expression-unrelated information's influence on FEA. Numerous experiments validate that the proposed method is promising in analysis of the differences in empathy ability between typically developing children and children with autism spectrum disorder.

中文翻译：

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面向儿童的移情能力分析：使用标签分布学习的联合面部表情识别和强度估计

移情能力是幼儿发展中最重要的社交沟通技巧之一。面部表情分析（FEA）是分析儿童共情能力的有效方法，因为它能够了解儿童的情绪状态。以前的工作主要集中在识别面部表情类别而未能估计表情强度，后者对于细粒度的情绪分析更为重要。为此，本文首先提出从面部表情的类别和强度来分析儿童的共情能力。提出了一种基于强度标签分布学习的新型 FEA 方法，旨在识别表达类别并在端到端框架中估计它们的强度水平。第一的，使用线性插值估计和高斯函数为表达序列中的每一帧生成强度标签分布，以解决表达强度缺乏合理注释的问题。然后，提出扩展强度标签分布以在多维表达空间中自动编码表达强度，旨在将表情识别和强度估计集成到一个统一的框架中，并通过抑制外观变化来提高表情识别性能。通过强度和强调弱表达之间的差异。最后，提出了一个类似Siamese的卷积神经网络，使用扩展的强度标签分布作为监督信息，从一对包含表达帧及其对应的中性帧的帧中学习表达模型，从而有效地消除了与表达无关的信息对表情的影响。有限元分析。大量实验证实，所提出的方法在分析典型发育儿童与自闭症谱系障碍儿童的共情能力差异方面很有前景。