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首页> 外文期刊>Journal of neuroscience, psychology, and economics >Automated Facial Coding: Validation of Basic Emotions and FACS AUs in FaceReader
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Automated Facial Coding: Validation of Basic Emotions and FACS AUs in FaceReader

机译：自动面部编码：FaceReader中基本情绪和FACS AU的验证

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In this study, we validated automated facial coding (AFC) software-FaceReader (Noldus, 2014)-on 2 publicly available and objective datasets of human expressions of basic emotions. We present the matching scores (accuracy) for recognition of facial expressions and the Facial Action Coding System (FACS) index of agreement. In 2005, matching scores of 89% were reported for FaceReader. However, previous research used a version of FaceReader that implemented older algorithms (version 1.0) and did not contain FACS classifiers. In this study, we tested the newest version (6.0). FaceReader recognized 88% of the target emotional labels in the Warsaw Set of Emotional Facial Expression Pictures (WSEFEP) and Amsterdam Dynamic Facial Expression Set (ADFES). The software reached a FACS index of agreement of 0.67 on average in both datasets. The results of this validation test are meaningful only in relation to human performance rates for both basic emotion recognition and FACS coding. The human emotions recognition for the 2 datasets was 85%, therefore FaceReader is as good at recognizing emotions as humans. To receive FACS certification, a human coder must reach an agreement of 0.70 with the master coding of the final test. Even though FaceReader did not attain this score, action units (AUs) 1, 2, 4, 5, 6, 9, 12, 15, and 25 might be used with high accuracy. We believe that FaceReader has proven to be a reliable indicator of basic emotions in the past decade and has a potential to become similarly robust with FACS.
机译：在这项研究中，我们验证了自动面部表情编码（AFC）软件-FaceReader（Noldus，2014年）-在2种公开可用的客观情感表达的基本情绪人类数据集上。我们提出了面部表情识别的匹配分数（准确性）和协议的面部动作编码系统（FACS）指数。 2005年，FaceReader的匹配分数为89％。但是，以前的研究使用的FaceReader版本实现了较旧的算法（1.0版），并且不包含FACS分类器。在本研究中，我们测试了最新版本（6.0）。 FaceReader识别了华沙情感面部表情图片集（WSEFEP）和阿姆斯特丹动态面部表情集（ADFES）中88％的目标情感标签。该软件在两个数据集中均达到0.67的FACS一致性指数。该验证测试的结果仅对于基本情感识别和FACS编码的人类绩效而言才有意义。这两个数据集的人类情感识别率为85％，因此FaceReader与人类一样擅长识别情感。要获得FACS认证，人类编码员必须与最终测试的主编码达成0.70的协议。即使FaceReader未达到此分数，也可以高精度使用动作单位（AU）1、2、4、5、6、9、12、15和25。我们认为，FaceReader在过去十年中已被证明是基本情绪的可靠指标，并且有可能在FACS中变得同样强大。

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