Recognition of facial expressions across various actors, contexts, and recording conditions in real-world videos involves identifying local facial movements. Hence, it is important to discover the formation of expressions from local representations captured from different parts of the face. So in this paper, we propose a dynamic kernel-based representation for facial expressions that assimilates facial movements captured using local spatio-temporal representations in a large universal Gaussian mixture model (uGMM). These dynamic kernels are used to preserve local similarities while handling global context changes for the same expression by utilizing the statistics of uGMM. We demonstrate the efficacy of dynamic kernel representation using three different dynamic kernels, namely, explicit mapping based, probability-based, and matching-based, on three standard facial expression datasets, namely, MMI, AFEW, and BP4D. Our evaluations show that probability-based kernels are the most discriminative among the dynamic kernels. However, in terms of computational complexity, intermediate matching kernels are more efficient as compared to the other two representations.

中文翻译：

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使用动态内核的视频中的面部表情识别。


识别现实视频中不同演员、背景和录制条件的面部表情涉及识别局部面部动作。因此，从面部不同部位捕获的局部表征中发现表情的形成非常重要。因此，在本文中，我们提出了一种基于动态内核的面部表情表示，该表示可以吸收使用大型通用高斯混合模型（uGMM）中的局部时空表示捕获的面部运动。这些动态内核用于保留局部相似性，同时利用 uGMM 的统计数据处理同一表达式的全局上下文更改。我们在三个标准面部表情数据集（即 MMI、AFEW 和 BP4D）上展示了使用三种不同动态内核（即基于显式映射、基于概率和基于匹配）的动态内核表示的功效。我们的评估表明，基于概率的内核是动态内核中最具辨别力的。然而，就计算复杂性而言，中间匹配内核比其他两种表示更有效。