DISFA: A Spontaneous Facial Action Intensity Database

By Seyed Mohammad Mavadati, Mohammad H. Mahoor, Kevin Bartlett, Philip Trinh, Jeffrey F. Cohn. IEEE Transactions on Affective Computing, 2013

792 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/e0867d523f610b32267fa7ec35a510936b8b595f

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

In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution (2020, methodology): “Then, we visualize the responses of the ﬁrst two feature channels using two samples from the DISFA database.”
AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups (2017, background): “The Denver Intensity of Spontaneous Facial Action (DISFA) database [26] consists of labeled stereo video recordings of 27 adults while watching a video clip.”
A Brief Review of Facial Emotion Recognition Based on Visual Information (2018, methodology): “Japanese Female Facial Expressions (JAFFE) [41]: The JAFFE database contains 213 images of seven facial emotions (six basic facial emotions and one neutral) posed by ten different female Japanese models.”
EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild (2016, methodology): “We tested the algorithm derived in Section 2 on three standard databases: the extended Cohn-Kanade database (CK+) [11], the Denver Intensity of Spontaneous Facial Action (DISFA) dataset [14], and the shoulder pain database of [12].”
Cross-dataset learning and person-specific normalisation for automatic Action Unit detection (2015, methodology): “[16].”
Modeling Stylized Character Expressions via Deep Learning (2016, methodology): “For human facial expression data collection, we combined publicly available annotated facial expression databases: extended CK+ [36], DISFA [37], KDEF [38] and MMI [39].”
Exploiting sparsity and co-occurrence structure for action unit recognition (2015, methodology): “• Superior performance over the state-of-the-art on the CK+ [19], G.-FERA [3], and DISFA [21] datasets.”
OpenFace 2.0: Facial Behavior Analysis Toolkit (2018, methodology): “The average concordance correlation coefﬁcient (CCC) on DISFA validation set across 12 AUs of OpenFace is 0 .”
OpenFace: An open source facial behavior analysis toolkit (2016, methodology): “In order to reduce the feature dimensionality we use a PCA model trained on a number of facial expression datasets: CK+ [38], DISFA [41], AVEC 2011 [52], FERA 2011 [60], and FERA 2015 [59].”
Gender and Smile Classification Using Deep Convolutional Neural Networks (2016, methodology): “[4] use CNN based method to achieve great a performance on the Denver Intensity of Spontaneous Facial Action (DISFA) [12] and exploit the smile-related region (mouth and cheek).”
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild (2017, methodology): “, DISFA [14], AM-FED [15]) or performing laboratorybased emotion inducing tasks (e.”
Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit Detection (2015, methodology): “Data: We have used the DISFA dataset [14] and the UNBCMcMaster Shoulder Pain Expression dataset [12] to perform our experiments.”
Every Smile is Unique: Landmark-Guided Diverse Smile Generation (2018, methodology): “We evaluate the proposed method on three public datasets: the UvA-NEMO Smile [9], the DISFA [27] and DISFA+ [26].”
BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database (2014, background): “Recent efforts to collect, annotate, and analyze spontaneous facial expression for community use have begun [26–28].”
Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition (2015, background): “SpontaneousAUs can be studied on the public DISFA [90] dataset as well as the partly public M3 (formerly RU-FACS) [10] andUNBC-McMaster [84] datasets.”
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications (2016, methodology): “This was recently facilitated by the publication of FE datasets that include intensity labels of spontaneous expression in RGB [202] and 3D [38].”
Deep Region and Multi-label Learning for Facial Action Unit Detection (2016, methodology): “Datasets: We evaluated DRML on two spontaneous datasets: BP4D [37] and DISFA [21].”