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Computer vision and machine learning are rapidly advancing fields of study. For better or worse, these tools have already permeated our everyday lives and are used for everything from auto-tagging social media images to curating what we view in our news feed. In this chapter, we discuss historical and contemporary approaches used to study face recognition, detection, manipulation, and generation. We frame our discussion within the context of how this work has been applied to the study of older adults, but also acknowledge that more work is needed both within this domain as well as at its intersection with, e.g., race and gender. Throughout the chapter we review, and at the end offer links to (Table 11.1), a number of resources that researchers can start using now in their research. We also discuss ongoing concerns related to the ethics of artificial intelligence and to using this emerging technology responsibly.
This chapter explains how facial expressions of emotions are produced and which physical features of the face convey emotional expressions. It talks about neural substrates of the perception of emotion from faces, addressing the brain regions involved and the temporal dynamics of their responses. The chapter discusses other aspects of faces that are related to emotion perception and elicitation. It reviews two main types of models that have been proposed to classify the facial expressions of emotions. In addition, the role of the amygdala extends to the perception of emotion from other cues than faces. The chapter further reviews the role of the main regions involved in the perception of emotions from faces. It talks about the pulvinar, and then describes the involvement of the regions classically associated with the perceptual analysis of faces. The chapter briefly mentions other cortical regions that have general roles in emotion.
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