The Chicago face database: A free stimulus set of faces and norming data

By Debbie S. Ma, Joshua Correll, Bernd Wittenbrink. Behavior Research Methods, 2015

1,663 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/c7884360037cb59d27ec25f8a3f00f2d95509066

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Citations in the corpus

Photorealistic Facial Texture Inference Using Deep Neural Networks (2016, methodology): “We use photographs from the Chicago Face Database [33] for this evaluation, and downsize/crop their resolution from 2444 × 1718 to 512 × 512 pixels.”
Contraction as a Response to Group Threat: Demographic Decline and Whites’ Classification of People Who Are Ambiguously White (2020, methodology): “For example, if White–Latino ambiguity were as prevalent in the U.S. population as it is in the CFD, then 6.84 percent of all Americans would be racially ambiguous across the White–Latino boundary.”
Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale (2022, positive): “For each of the ‘official’ two gender and three race categories (male, female, White, Black, Asian), we take the corresponding part of the Chicago Face Dataset, a dataset of faces with self-identified gender and race (Ma et al., 2015), feed these to the component, and average the representations of ”
The Caucasian and North African French Faces (CaNAFF): A Face Database (2018, background): “A second limitation is that, compared to some other databases (e.g. Chicago Face Database; Ma et al., 2015), the CaNAFF only contains young male faces.”
Adults and children implicitly associate brilliance with men more than women (2020, methodology): “stereotypes by using pictures of Black females and males (n = 8 each) from the Chicago Face Database (Ma et al., 2015), matched in attractiveness, age, and positive emotionality (see Appendix S1 in the SOM).”
Accurate and Robust Neural Networks for Security Related Applications Exampled by Face Morphing Attacks (2018, neutral): “Database BU-4DFE[20] CFD[21] FEI[22] FERET[23] PUT[24]”
APDrawingGAN: Generating Artistic Portrait Drawings From Face Photos With Hierarchical GANs (2019, methodology): “Face photos are from the datasets of CFD [21] and Siblings [36].”
paGAN (2018, positive): “We choose subjects that have multiple expressions within the dataset [Ma et al. 2015] and use the rest for testing.”