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The Role of Low-Spatial Frequency Components in the Processing of Deceptive Faces: A Study Using Artificial Face Models
Author(s) -
Ken Kihara,
Yuji Takeda
Publication year - 2019
Publication title -
frontiers in psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.947
H-Index - 110
ISSN - 1664-1078
DOI - 10.3389/fpsyg.2019.01468
Subject(s) - happiness , psychology , facial expression , anger , face (sociological concept) , expression (computer science) , task (project management) , emotion classification , cognitive psychology , facial recognition system , emotion detection , emotional expression , emotion recognition , artificial intelligence , pattern recognition (psychology) , communication , social psychology , computer science , linguistics , neuroscience , philosophy , management , economics , programming language
Interpreting another’s true emotion is important for social communication, even in the face of deceptive facial cues. Because spatial frequency components provide important clues for recognizing facial expressions, we investigated how we use spatial frequency information from deceptive faces to interpret true emotion. We conducted two different tasks: a face-generating experiment in which participants were asked to generate deceptive and genuine faces by tuning the intensity of happy and angry expressions (Experiment 1) and a face-classification task in which participants had to classify presented faces as either deceptive or genuine (Experiment 2). Low- and high-spatial frequency (LSF and HSF) components were varied independently. The results showed that deceptive happiness (i.e., anger is the hidden expression) involved different intensities for LSF and HSF. These results suggest that we can identify hidden anger by perceiving unbalanced intensities of emotional expression between LSF and HSF information contained in deceptive faces.

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