Abstract
In this paper, we applied a reverse correlation approach to study the features that humans use to categorize facial expressions. The well-known portrait of Mona Lisa was used as the base image to investigate the features differentiating happy and sad expressions. The base image was blended with sinusoidal noise masks to create the stimulus. Observers were required to view each image and categorized it as happy or sad. Analysis of responses using superimposed classification images revealed both locations and identity of information required to represent each certain expression. To further investigate the results, a neural network based classifier was developed to identify the expression of the superimposed images from the machine learning perspective, which reveals that the pattern which humans perceive the expression is acknowledged by machines.
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Zhang, X., Yin, L., Hipp, D., Gerhardstein, P. (2014). Evaluation of Perceptual Biases in Facial Expression Recognition by Humans and Machines. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_78
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DOI: https://doi.org/10.1007/978-3-319-14364-4_78
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14363-7
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