Predicting and visualizing psychological attributions with a deep neural network
[S.l.] : Institute of Electrical and Electronics Engineers (IEEE)
InPattern Recognition (ICPR), 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 1-6
2016 23rd International Conference on Pattern Recognition (ICPR) (Cancún, Mexico, December 4-8, 2016)
Article in monograph or in proceedings
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SW OZ DCC KI
Pattern Recognition (ICPR), 2016 23rd International Conference on Pattern Recognition (ICPR)
SubjectCognitive artificial intelligence; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication; Radboudumc 13: Stress-related disorders DCMN: Donders Center for Medical Neuroscience
Judgments about personality based on facial appearance are strong effectors in social decision making, and are known to have impact on areas from presidential elections to jury decisions. Recent work has shown that it is possible to predict perception of memorability, trustworthiness, intelligence and other attributes in human face images. The most successful of these approaches require face images expertly annotated with key facial landmarks. We demonstrate a Convolutional Neural Network (CNN) model that is able to perform the same task without the need for landmark features, thereby greatly increasing efficiency. The model has high accuracy, surpassing human-level performance in some cases. Furthermore, we use a deconvolutional approach to visualize important features for perception of 22 attributes and demonstrate a new method for separately visualizing positive and negative features.
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