Automated Facial Trait Judgment and Election Outcome Prediction: Social Dimensions of Face

By Jungseock Joo, Francis F. Steen, Song-Chun Zhu. 2015

72 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Claire Cardie
2. Jungseock Joo
3. Adriana Kovashka
4. Zuxuan Wu
5. Ser-Nam Lim
6. Serge J. Belongie
7. Kimmo Kärkkäinen
8. Christopher Thomas

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Zuxuan Wu
3. Kimmo Kärkkäinen
4. Jungseock Joo
5. Ser-Nam Lim
6. Dacheng Tao
7. Yi Yang
8. Ming-Hsuan Yang

In Experimental and Cognitive Psychology

1. Adriana Kovashka
2. David Masip
3. Claire Cardie
4. Jungseock Joo
5. Àgata Lapedriza
6. Carles Ventura
7. Christopher Thomas

In Social Sciences & Humanities

1. Kimmo Kärkkäinen
2. Jungseock Joo
3. Serge J. Belongie

Citations in the corpus

• FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age (2019, background): “Many existing face datasets have relied on photographs of public figures such as politicians or celebrities [31, 23, 24, 46, 37].”
• Persuasive Faces: Generating Faces in Advertisements (2018, background): “[13] learned to predict whether a photograph portrays a politician in a positive or negative light, and [14] trained classifiers to predict the outcomes of elections based on the candidates’ faces, but neither of these works creates a generative model.”
• Protest Activity Detection and Perceived Violence Estimation from Social Media Images (2017, background): “Researchers have also analyzed photographs of politicians shared on social media [51] or their perceived personalities from facial appearance [22].”
• Exploring Visual Engagement Signals for Representation Learning (2021, background): “This area includes (1) detecting cyberbullying and hate speech [29, 71, 20, 40], (2) identifying emotions [43, 1, 59, 62, 81], (3) understanding rhetoric and intentions [36, 37, 70, 30, 31, 88, 74, 45, 34].”
• Understanding the Political Ideology of Legislators from Social Media Images (2019, background): “For example, Joo, Steen, and Zhu (2015) shows that Republican and Democratic politicians can be distinguished from their facial appearance by a hierarchical discriminative model.”
• Interpreting CNN Models for Apparent Personality Trait Regression (2017, background): “[11] proposed a ranking SVM modeling on intermediate features to predict facial trait judgments, and focused on Figure 2.”
• FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation (2021, background): “Many existing face datasets have been sourced from photographs of public figures such as politicians or celebrities [31, 21, 23, 48, 37].”