Towards More Robust Automatic Facial Expression Recognition in Smart Environments

Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments Pub Date : 2017-06-21 DOI:10.1145/3056540.3056546

Arne Bernin, Larissa Müller, Sobin Ghose, K. Luck, C. Grecos, Qi Wang, Florian Vogt

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引用次数: 19

Abstract

In this paper, we provide insights towards achieving more robust automatic facial expression recognition in smart environments based on our benchmark with three labeled facial expression databases. These databases are selected to test for desktop, 3D and smart environment application scenarios. This work is meant to provide a neutral comparison and guidelines for developers and researchers interested to integrate facial emotion recognition technologies in their applications, understand its limitations and adaptation as well as enhancement strategies. We also introduce and compare three different metrics for finding the primary expression in a time window of a displayed emotion. In addition, we outline facial emotion recognition limitations and enhancements for smart environments and non-frontal setups. By providing our comparison and enhancements we hope to build a bridge from affective computing research and solution providers to application developers that like to enhance new applications by including emotion based user modeling.

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在智能环境中实现更鲁棒的自动面部表情识别

在本文中，我们基于三个标记面部表情数据库的基准，为在智能环境中实现更强大的自动面部表情识别提供了见解。选择这些数据库进行桌面、3D和智能环境应用场景的测试。这项工作旨在为有兴趣在其应用中集成面部情感识别技术的开发人员和研究人员提供中立的比较和指导，了解其局限性和适应性以及增强策略。我们还介绍并比较了三种不同的指标，用于在显示的情绪的时间窗口中寻找主要表达。此外，我们概述了智能环境和非正面设置的面部情感识别限制和增强。通过提供我们的比较和增强，我们希望在情感计算研究和解决方案提供商与应用程序开发人员之间建立一座桥梁，这些开发人员喜欢通过包含基于情感的用户建模来增强新应用程序。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments

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