Cross Marie P, Acevedo Amanda M, Hunter John F
Department of Biobehavioral Health, Pennsylvania State University, University Park, PA USA.
Basic Biobehavioral and Psychological Sciences Branch, National Cancer Institute, Rockville, MD USA.
Affect Sci. 2023 Jul 10;4(3):500-505. doi: 10.1007/s42761-023-00195-0. eCollection 2023 Sep.
Facial expression recognition software is becoming more commonly used by affective scientists to measure facial expressions. Although the use of this software has exciting implications, there are persistent and concerning issues regarding the validity and reliability of these programs. In this paper, we highlight three of these issues: biases of the programs against certain skin colors and genders; the common inability of these programs to capture facial expressions made in non-idealized conditions (e.g., "in the wild"); and programs being forced to adopt the underlying assumptions of the specific theory of emotion on which each software is based. We then discuss three directions for the future of affective science in the area of automated facial coding. First, researchers need to be cognizant of exactly how and on which data sets the machine learning algorithms underlying these programs are being trained. In addition, there are several ethical considerations, such as privacy and data storage, surrounding the use of facial expression recognition programs. Finally, researchers should consider collecting additional emotion data, such as body language, and combine these data with facial expression data in order to achieve a more comprehensive picture of complex human emotions. Facial expression recognition programs are an excellent method of collecting facial expression data, but affective scientists should ensure that they recognize the limitations and ethical implications of these programs.
情感科学家越来越普遍地使用面部表情识别软件来测量面部表情。尽管使用这种软件有令人兴奋的意义，但这些程序的有效性和可靠性仍存在一些持续且令人担忧的问题。在本文中，我们突出了其中三个问题：程序对某些肤色和性别的偏见；这些程序通常无法捕捉在非理想化条件下（例如“在自然环境中”）做出的面部表情；以及程序被迫采用每个软件所基于的特定情感理论的潜在假设。然后，我们讨论了情感科学在自动面部编码领域未来的三个方向。首先，研究人员需要确切了解这些程序所基于的机器学习算法是如何以及在哪些数据集上进行训练的。此外，围绕面部表情识别程序的使用存在一些伦理考量，例如隐私和数据存储。最后，研究人员应考虑收集额外的情感数据，如肢体语言，并将这些数据与面部表情数据相结合，以便更全面地了解复杂的人类情感。面部表情识别程序是收集面部表情数据的一种出色方法，但情感科学家应确保他们认识到这些程序的局限性和伦理影响。
