Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICPR.2016.7899598 | 2016 23rd International Conference on Pattern Recognition (ICPR) |
Keywords | Field | DocType |
psychological attribution visualization,psychological attribution prediction,deep neural network,facial appearance,social decision making,human face image annotation,key facial landmarks,CNN model,convolutional neural network model,human-level performance,deconvolutional approach | Pattern recognition,Convolutional neural network,Trustworthiness,Computer science,Attribution,Artificial intelligence,Landmark,Artificial neural network,Social decision making,Perception,Machine learning,Personality | Conference |
ISSN | ISBN | Citations |
1051-4651 | 978-1-5090-4848-9 | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edward Grant | 1 | 0 | 0.68 |
| Stephan Sahm | 2 | 0 | 0.34 |
| Mariam Zabihi | 3 | 0 | 0.34 |
| Marcel Van Gerven | 4 | 321 | 39.35 |