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Eigenface Algorithm-Based Facial Expression Recognition in Conversations - An Experimental Study

机译:基于特征脸算法的会话面部表情识别实验研究

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Recognizing facial expressions is important in many fields such as computer-human interface. Though different approaches have been widely used in facial expression recognition systems, there are still many problems in practice to achieve the best implementation outcomes. Most systems are tested via the lab-based facial expressions, which may be unnatural. Particularly many systems have problems when they are used for recognizing the facial expressions being used during conversation. This paper mainly conducts an experimental study on Eigenface algorithm-based facial expression recognition. It primarily aims to investigate the performance of both lab-based facial expressions and facial expressions used during conversation. The experiment also aims to probe the problems arising from the recognition of facial expression in conversations. The study is carried out using both the author's facial expressions as the basis for the lab-based expressions and the facial expressions from one elderly person during conversation. The experiment showed a good result in lab-based facial expressions, but there are some issues observed when using the case of facial expressions obtained in conversation. By analyzing the experimental results, future research focus has been highlighted as the investigation of how to recognize special emotions such as a wry smile and how to deal with the interferences in the lower part efface when speaking.
机译:识别面部表情在许多领域中都很重要,例如计算机人机界面。尽管在面部表情识别系统中已广泛使用了不同的方法,但在实践中仍然存在许多问题,以实现最佳的实现效果。大多数系统都是通过基于实验室的面部表情进行测试的,这可能是不自然的。特别地,当许多系统用于识别在对话期间正在使用的面部表情时,它们会出现问题。本文主要对基于特征脸算法的人脸表情识别进行实验研究。它的主要目的是研究基于实验室的面部表情和会话期间使用的面部表情的性能。该实验还旨在探讨对话中面部表情识别所引起的问题。这项研究使用作者的面部表情作为基于实验室的表情的基础,以及谈话中一位老人的面部表情进行。该实验在基于实验室的面部表情上显示出良好的结果,但是在使用通过对话获取的面部表情的情况下,发现了一些问题。通过对实验结果的分析,未来的研究重点已集中在如何识别特殊的情绪(如扭曲的笑容)以及如何应对说话时下部面部的干扰等方面。

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