Li Yen-Ting, Yeh Su-Ling, Huang Tsung-Ren
Department of Psychology, National Taiwan University, Taipei City, Taiwan.
Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei City, Taiwan.
Front Psychol. 2023 Dec 7;14:1201145. doi: 10.3389/fpsyg.2023.1201145. eCollection 2023.
Emotion has been a subject undergoing intensive research in psychology and cognitive neuroscience over several decades. Recently, more and more studies of emotion have adopted automatic rather than manual methods of facial emotion recognition to analyze images or videos of human faces. Compared to manual methods, these computer-vision-based, automatic methods can help objectively and rapidly analyze a large amount of data. These automatic methods have also been validated and believed to be accurate in their judgments. However, these automatic methods often rely on statistical learning models (e.g., deep neural networks), which are intrinsically inductive and thus suffer from problems of induction. Specifically, the models that were trained primarily on Western faces may not generalize well to accurately judge Eastern faces, which can then jeopardize the measurement invariance of emotions in cross-cultural studies. To demonstrate such a possibility, the present study carries out a cross-racial validation of two popular facial emotion recognition systems-FaceReader and DeepFace-using two Western and two Eastern face datasets. Although both systems could achieve overall high accuracies in the judgments of emotion category on the Western datasets, they performed relatively poorly on the Eastern datasets, especially in recognition of negative emotions. While these results caution the use of these automatic methods of emotion recognition on non-Western faces, the results also suggest that the measurements of happiness outputted by these automatic methods are accurate and invariant across races and hence can still be utilized for cross-cultural studies of positive psychology.
几十年来，情感一直是心理学和认知神经科学领域深入研究的课题。最近，越来越多的情感研究采用自动而非人工的面部情感识别方法来分析人脸图像或视频。与人工方法相比，这些基于计算机视觉的自动方法有助于客观、快速地分析大量数据。这些自动方法也经过了验证，并且被认为判断准确。然而，这些自动方法通常依赖于统计学习模型（例如深度神经网络），而这些模型本质上是归纳性的，因此存在归纳问题。具体而言，主要基于西方人脸训练的模型可能无法很好地推广到准确判断东方人脸，这可能会危及跨文化研究中情感测量的不变性。为了证明这种可能性，本研究使用两个西方人脸数据集和两个东方人脸数据集，对两个流行的面部情感识别系统——FaceReader和DeepFace——进行了跨种族验证。虽然这两个系统在西方数据集的情感类别判断上都能达到总体较高的准确率，但它们在东方数据集上的表现相对较差，尤其是在识别负面情绪方面。这些结果提醒人们在非西方人脸的情感识别中要谨慎使用这些自动方法，同时也表明这些自动方法输出的幸福度测量在不同种族间是准确且不变的，因此仍可用于积极心理学的跨文化研究。
