Presentation and validation of the Radboud Faces Database

By O. Langner, Ron Dotsch, Gijsbert Bijlstra, Daniël H. J. Wigboldus, Skyler T. Hawk, A. van Knippenberg. 2010

2,454 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation (2017, background): “The Radboud Faces Database (RaFD) [12] consists of 4,824 images collected from 67 participants.”
Emotional facial expressions evoke faster orienting responses, but weaker emotional responses at neural and behavioural levels compared to scenes: A simultaneous EEG and facial EMG study (2016, positive): “The 270 happy, fearful and neutral face stimuli were taken from the Radboud Faces Database (RAFD; Langner et al., 2010) and Set A of the Karolinska Directed Emotional Faces Database (KDEF; Lundqvist et al.”
Addressing bias in machine learning algorithms: A pilot study on emotion recognition for intelligent systems (2017, methodology): “The four datasets were the NIMH Child Emotional Faces Picture Set (NIMH-ChEFS) [16], the Dartmouth Database of Children’s Faces [14], the Radboud Faces Database [19], and the Child Emotions Picture Set (CEPS) [20] (Figure 1).”
paGAN (2018, positive): “• 67 subjects, each with 3 gazes and 3 camera angles per each of 8 expressions, from the Radbound Faces dataset [Langner et al. 2010].”
Deep Facial Expression Recognition: A Survey (2018, background): “RaFD [41] 1,608 images 67 Lab P seven basic expressions”
Demystifying Inter-Class Disentanglement (2019, background): “RaFD (Langner et al., 2010) : RaFD consists of 4,824 images collected from 67 participants making eight facial expressions in three different gaze directions, which are captured from three different angles.”
CycleMorph: Cycle Consistent Unsupervised Deformable Image Registration (2020, methodology): “A. Datasets 1) Facial Expression Image: The 2D face expression images are obtained from Radboud Faces Database (RaFD) [32].”
GANimation: Anatomically-aware Facial Animation from a Single Image (2018, methodology): “[4] is trained on the RaFD [16] dataset which has only 8 binary labels for facial expressions, namely sad, neutral, angry, contemptuous, disgusted, surprised, fearful and happy.”
The Caucasian and North African French Faces (CaNAFF): A Face Database (2018, methodology): “By having easy (and free) access to this database (all the information are available on this link: https://osf. io/274ry/), researchers will have an alternative to the use of foreign databases (e.g. the Radboud Faces Database; Langner et al., 2010) or unstandardised stimuli (e.g. stimuli found on se”
How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis (2020, methodology): “We saw the “objective” portrayal of visible identifications fall apart in cases like MORPH, where the authors described the difficulty “to guess [some subjects’] race from the photos” [19].”
Diffusion models in medical imaging: A comprehensive survey (2022, result): “The experimental results affirm the high accuracy of the proposed method in registering both 2D facial expressions [100] and 3D medical images [101, 102].”