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首页> 外文会议>International Conference on Human-Computer Interaction;International Conference on Universal Access in Human-Computer Interaction >Building an Ecologically Valid Facial Expression Database-Behind the Scenes
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Building an Ecologically Valid Facial Expression Database-Behind the Scenes

机译：构建生态有效的面部表情数据库 - 在幕后

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Artificial Intelligence (AI) algorithms, together with a general increased computational performance, allow nowadays exploring the use of Facial Expression Recognition (FER) as a method of recognizing human emotion through the use of neural networks. The interest in facial emotion and expression recognition in real-life situations is one of the current cutting-edge research challenges. In this context, the creation of an ecologically valid facial expression database is crucial. To this aim, a controlled experiment has been designed, in which thirty-five subjects aged 18-35 were asked to react spontaneously to a set of 48 validated images from two affective databases, IAPS and GAPED. According to the Self-Assessment Manikin, participants were asked to rate images on a 9-points visual scale on valence and arousal. Furthermore, they were asked to select one of the six Ekman's basic emotions. During the experiment, an RGB-D camera was also used to record spontaneous facial expressions aroused in participants storing both the color and the depth frames to feed a Convolutional Neural Network (CNN) to perform FER. In every case, the prevalent emotion pointed out in the questionnaires matched with the expected emotion. CNN obtained a recognition rate of 75.02%, computed comparing the neural network results with the evaluations given by a human observer. These preliminary results have confirmed that this experimental setting is an effective starting point for building an ecologically valid database.
机译：人工智能（AI）算法以及一般的计算性能，允许现在探讨面部表情识别（FER）作为通过使用神经网络来识别人类情绪的方法。现实情况中对面部情感和表达识别的兴趣是当前的尖端研究挑战之一。在这种情况下，创建生态有效的面部表情数据库至关重要。为此目的，设计了一种受控实验，其中，18-35岁的三十五次受试者被要求自发地从两个情感数据库，IAP和缺点自发地反应。根据自我评估Manikin，要求参与者在价值和唤醒的9分视觉规模上进行评分图像。此外，他们被要求选择六位ekman的基本情绪之一。在实验期间，RGB-D相机还用于记录在存储颜色和深度帧的参与者中引起的自发面部表达，以馈送卷积神经网络（CNN）以执行FER。在每种情况下，在调查问卷中指出的普遍情绪与预期的情感相匹配。 CNN获得了75.02％的识别率，计算了与人类观察者给出的评估进行了评估的神经网络结果。这些初步结果证实了该实验设置是构建生态有效数据库的有效起点。

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