Defining Standard Values for FaceReader Facial Expression Software Output

FaceReader is a validated software package that uses computer vision technology for facial expression recognition which has become increasingly popular in academic research to expedite, scale, and decrease the cost of facial emotion analysis. In this study, we compare FaceReader analysis to human evaluator interpretation in order to define standard values for the software output.

Randomly generated facial images produced by generative adversarial networks were analyzed using FaceReader and by survey participants (n=496). The age, facial emotion, and intensity of emotion as determined by the software and survey participants were recorded. Results were analyzed and compared.

80 randomly generated images (20 children, 20 young adult, 20 middle aged, and 20 elderly; 38 male and 42 female) were included. Analysis of correlation between most common expression identified by FaceReader and the primary emotion detected by surveyors showed strong correlation (κ = 0.77, 95% CI = 0.64-0.91). On analyzing this correlation by age group, there was fair correlation in children (κ = 0.40, 95% CI = 0.078-0.72), perfect correlation in young adults (κ = 1.0, 95% CI = 1.0-1.0), strong correlation in middle aged adults (κ = 0.79, 95% CI = 0.53-1) and near perfect in elderly adults(κ = 0.9 , 95% CI = 0.7-1.0).

We provided the first study defining the expected average values generated by FaceReader in generally smiling images. This can be used as a standard in future studies.

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