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Decoding facial expressions that produce emotion valence ratings with human-like accuracy

Haines, Nathaniel

Abstract Details

2017, Master of Arts, Ohio State University, Psychology.
Facial expressions are fundamental to human interaction, including the conveyance of threat, cooperative intent, and internal emotional states. In research settings, facial expressions are typically coded manually by trained human coders; however, advances in computer vision and machine learning (CVML) allow for a more efficient and less labor-intensive alternative. Unfortunately, current CVML implementations achieve only moderate accuracy for rating positive and negative affect intensity; this limitation has limited the adoption of these models. Here, using over 6,000 video recordings from human subjects, we show that CVML models rate positive and negative emotion intensity with human-like accuracy. Additionally, we show that these same models identify theoretically meaningful patterns of facial movement that are strongly associated with human ratings at the individual-subject level. Our results suggest that CVML both provides an efficient method to automate valence intensity coding and rapidly identifies individual differences in facial expression recognition.
Woo-Young Ahn (Committee Member)
Theodore Beauchaine (Advisor)
Jennifer Cheavens (Committee Member)
45 p.

Recommended Citations

Citations

  • Haines, N. (2017). Decoding facial expressions that produce emotion valence ratings with human-like accuracy [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511257717736851

    APA Style (7th edition)

  • Haines, Nathaniel. Decoding facial expressions that produce emotion valence ratings with human-like accuracy . 2017. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1511257717736851.

    MLA Style (8th edition)

  • Haines, Nathaniel. "Decoding facial expressions that produce emotion valence ratings with human-like accuracy ." Master's thesis, Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511257717736851

    Chicago Manual of Style (17th edition)