Discriminant Functional Learning of Color Features for the Recognition of Facial Action Units and Their Intensities

The promises and perils of automated facial action coding in studying children's emotions

Computer vision algorithms have made tremendous advances in recent years. We now have algorithms that can detect and recognize objects, faces, and even facial actions in still images and video sequences. This is wonderful news for researchers that need to code facial articulations in large data sets of images and videos, because this task is time consuming and can only be completed by expert coders, making it very expensive. The availability of computer algorithms that can automatically code facial actions in extremely large data sets also opens the door to studies in psychology and neuroscience that were not previously possible, for example, to study the development of the production of facial expressions from infancy to adulthood within and across cultures. Unfortunately, there is a lack of methodological understanding on how these algorithms should and should not be used, and on how to select the most appropriate algorithm for each study. This article aims to address this gap in the literature. Specifically, we present several methodologies for use in hypothesis-based and exploratory studies, explain how to select the computer algorithms that best fit to the requirements of our experimental design, and detail how to evaluate whether the automatic annotations provided by existing algorithms are trustworthy. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements

It is commonly assumed that a person's emotional state can be readily inferred from his or her facial movements, typically called emotional expressions or facial expressions. This assumption influences legal judgments, policy decisions, national security protocols, and educational practices; guides the diagnosis and treatment of psychiatric illness, as well as the development of commercial applications; and pervades everyday social interactions as well as research in other scientific fields such as artificial intelligence, neuroscience, and computer vision. In this article, we survey examples of this widespread assumption, which we refer to as the common view, and we then examine the scientific evidence that tests this view, focusing on the six most popular emotion categories used by consumers of emotion research: anger, disgust, fear, happiness, sadness, and surprise. The available scientific evidence suggests that people do sometimes smile when happy, frown when sad, scowl when angry, and so on, as proposed by the common view, more than what would be expected by chance. Yet how people communicate anger, disgust, fear, happiness, sadness, and surprise varies substantially across cultures, situations, and even across people within a single situation. Furthermore, similar configurations of facial movements variably express instances of more than one emotion category. In fact, a given configuration of facial movements, such as a scowl, often communicates something other than an emotional state. Scientists agree that facial movements convey a range of information and are important for social communication, emotional or otherwise. But our review suggests an urgent need for research that examines how people actually move their faces to express emotions and other social information in the variety of contexts that make up everyday life, as well as careful study of the mechanisms by which people perceive instances of emotion in one another. We make specific research recommendations that will yield a more valid picture of how people move their faces to express emotions and how they infer emotional meaning from facial movements in situations of everyday life. This research is crucial to provide consumers of emotion research with the translational information they require.