Frontiers in Psychology (Sep 2015)
Automated Facial Coding Software Outperforms People in Recognizing Neutral Faces as Neutral from Standardized Datasets
Abstract
Little is known about people’s accuracy of recognizing neutral faces as neutral. In this paper, I demonstrate the importance of knowing how well people recognize neutral faces. I contrasted human recognition scores of 100 typical, neutral front-up facial images with scores of an arguably objective judge – automated facial coding (AFC) software. I hypothesized that the software would outperform humans in recognizing neutral faces because of the inherently objective nature of computer algorithms. Results confirmed this hypothesis. I provided the first-ever evidence that computer software (90%) was more accurate in recognizing neutral faces than people were (59%). I posited two theoretical mechanisms, i.e. smile-as-a-baseline and false recognition of emotion, as possible explanations for my findings.
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