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Novel dynamic Bayesian networks for facial action element recognition and understanding

机译：用于面部动作元素识别和理解的新型动态贝叶斯网络

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摘要

In daily life, language is an important tool of communication between people. Besides language, facial action can also provide a great amount of information. Therefore, facial action recognition has become a popular research topic in the field of human-computer interaction (HCI). However, facial action recognition is quite a challenging task due to its complexity. In a literal sense, there are thousands of facial muscular move-ments, many of which have very subtle differences. Moreover, muscular movements always occur simultaneously when the pose is changed. To address this problem, we first build a fully automatic facial points detection system based on a local Gabor filter bank and principal component anal-ysis. Then, novel dynamic Bayesian networks are proposed to perform fa-cial action recognition using the junction tree algorithm over a limited num-ber of feature points. In order to evaluate the proposed method, we have used the Korean face database for model training. For testing, we used the CUbiC FacePix, facial expressions and emotion database, Japanese fe-male facial expression database, and our own database. Our experimental results clearly demonstrate the feasibility of the proposed approach.
机译：在日常生活中，语言是人与人之间交流的重要工具。除语言外，面部动作还可以提供大量信息。因此，面部动作识别已成为人机交互（HCI）领域的热门研究主题。然而，由于其复杂性，面部动作识别是一项非常具有挑战性的任务。从字面上看，有数千种面部肌肉运动，其中许多具有非常微妙的差异。而且，当姿势改变时，肌肉运动总是同时发生。为了解决这个问题，我们首先建立了一个基于局部Gabor滤波器组和主成分分析的全自动面部点检测系统。然后，提出了新颖的动态贝叶斯网络，以在有限数量的特征点上使用结点树算法执行面部动作识别。为了评估该方法，我们使用了韩国人脸数据库进行模型训练。为了进行测试，我们使用了CUbiC FacePix，面部表情和情感数据库，日本女性男性面部表情数据库以及我们自己的数据库。我们的实验结果清楚地证明了该方法的可行性。

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