Modelling perceptions of criminality and remorse from faces using a data-driven computational approach

ABSTRACT

Perceptions of criminality and remorse are critical for legal decision-making. While faces perceived as criminal are more likely to be selected in police lineups and to receive guilty verdicts, faces perceived as remorseful are more likely to receive less severe punishment recommendations. To identify the information that makes a face appear criminal and/or remorseful, we successfully used two different data-driven computational approaches that led to convergent findings: one relying on the use of computer-generated faces, and the other on photographs of people. In addition to visualising and validating the perceived looks of criminality and remorse, we report correlations with earlier face models of dominance, threat, trustworthiness, masculinity/femininity, and sadness. The new face models of criminal and remorseful appearance contribute to our understanding of perceived criminality and remorse. They can be used to study the effects of perceived criminality and remorse on decision-making; research that can ultimately inform legal policies.

KEYWORDS:

• Social perception
• faces
• criminal appearance
• remorse
• emotion
• data-driven models

Acknowledgments

We thank Virginia Falvello, Lauren Feldman, and Matthias Keller for excellent research assistance.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

This research was partially supported by the Swiss National Science Foundation [grant number 100014_135213].