Datasets
- CVPR 2023: 5th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW)
- ECCV 2022: 4th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW)
- CVPR 2022: 3rd Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW)
- ICCV 2021: 2nd Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW)
- Synthesizing Coupled 3D Face Modalities by TBGAN
- Face Bio-metrics under COVID (Masked Face Recognition Challenge & Workshop ICCV 2021)
- First Affect-in-the-Wild Challenge
- FG-2020 Competition: Affective Behavior Analysis in-the-wild (ABAW)
- Aff-Wild2 database
- First Faces in-the-wild Workshop-Challenge
- In-The-Wild 3D Morphable Models: Code and Data
- Sound of Pixels
- Lightweight Face Recognition Challenge & Workshop (ICCV 2019)
- Audiovisual Database of Normal-Whispered-Silent Speech
- Deformable Models of Ears in-the-wild for Alignment and Recognition
- 300 Videos in the Wild (300-VW) Challenge & Workshop (ICCV 2015)
- 1st 3D Face Tracking in-the-wild Competition
- The Fabrics Dataset
- The Mobiface Dataset
- Large Scale Facial Model (LSFM)
- AgeDB
- AFEW-VA Database for Valence and Arousal Estimation In-The-Wild
- The CONFER Database
- Special Issue on Behavior Analysis "in-the-wild"
- Body Pose Annotations Correction (CVPR 2016)
- KANFace
- MeDigital
- FG-2020 Workshop "Affect Recognition in-the-wild: Uni/Multi-Modal Analysis & VA-AU-Expression Challenges"
- 4DFAB: A Large Scale 4D Face Database for Expression Analysis and Biometric Applications
- Affect "in-the-wild" Workshop
- 2nd Facial Landmark Localisation Competition - The Menpo BenchMark
- Facial Expression Recognition and Analysis Challenge 2015
- The SEWA Database
- 300 Faces In-The-Wild Challenge (300-W), IMAVIS 2014
- Mimic Me
- MAHNOB-HCI-Tagging database
- 300 Faces In-the-Wild Challenge (300-W), ICCV 2013
- MAHNOB Laughter database
- MAHNOB MHI-Mimicry database
- Facial point annotations
- MMI Facial expression database
- SEMAINE database
- iBugMask: Face Parsing in the Wild (ImaVis 2021)
- iBUG Eye Segmentation Dataset
Code
- Valence/Arousal Online Annotation Tool
- The Menpo Project
- The Dynamic Ordinal Classification (DOC) Toolbox
- Gauss-Newton Deformable Part Models for Face Alignment in-the-Wild (CVPR 2014)
- Robust and Efficient Parametric Face/Object Alignment (2011)
- Discriminative Response Map Fitting (DRMF 2013)
- End-to-End Lipreading
- DS-GPLVM (TIP 2015)
- Subspace Learning from Image Gradient Orientations (2011)
- Discriminant Incoherent Component Analysis (IEEE-TIP 2016)
- AOMs Generic Face Alignment (2012)
- Fitting AAMs in-the-Wild (ICCV 2013)
- Salient Point Detector (2006/2008)
- Facial point detector (2010/2013)
- Chehra Face Tracker (CVPR 2014)
- Empirical Analysis Of Cascade Deformable Models For Multi-View Face Detection (IMAVIS 2015)
- Continuous-time Prediction of Dimensional Behavior/Affect
- Real-time Face tracking with CUDA (MMSys 2014)
- Facial Point detector (2005/2007)
- Facial tracker (2011)
- Salient Point Detector (2010)
- AU detector (TAUD 2011)
- Action Unit Detector (2016)
- AU detector (LAUD 2010)
- Smile Detectors
- Head Nod Shake Detector (2010/2011)
- Gesture Detector (2011)
- Head Nod Shake Detector and 5 Dimensional Emotion Predictor (2010/2011)
- Gesture Detector (2010)
- HCI^2 Framework
- FROG Facial Tracking Component
- SEMAINE Visual Components (2008/2009)
- SEMAINE Visual Components (2009/2010)
The CONFER Database
The Conflict Escalation Resolution (CONFER) Database is a collection of audio-visual recordings of naturalistic interactions from televised political debates suitable for the investigation of conflict behavior as well as other social signals and behaviours. It contains 120 audio-visual episodes in Greek language extracted from a total of 27 TV broadcasts, with total duration of approximately 142 minutes and a total number of 54 subjects (43 male, 11 female). The dataset is split into 2 sets which consist of recordings containing interactions that involve two or three participants, respectively. All 120 clips have been annotated by 10 experts in terms of continuous conflict intensity.
The database has been collected within the IEF Marie Curie Fellowship (CONFER) of Yannis Panagakis and EC H2020 SEWA project (http://www.sewaproject.eu) and is freely available.
The dataset can be downloaded from the links below:
README.pdf
Content: README file
Format: PDF document
Size: 139 kB
Protocol.zip
Content: List of clip names belonging to the 5 folds of the baseline cross-validation experiment for each set (two and three) of the database
Format: zip archive containing text files
Size: 6 kB
Clips
Content: 120 audio-visual clips of the database
Format: zip archive containing avi videos
Clips/two/FOLD_1.zip Size: 3.9 GB
Clips/two/FOLD_2.zip Size: 2.1 GB
Clips/two/FOLD_3.zip Size: 2.7 GB
Clips/two/FOLD_4.zip Size: 2.7 GB
Clips/two/FOLD_5.zip Size: 3.4 GB
Clips/three/FOLD_1.zip Size: 7.7 GB
Clips/three/FOLD_2.zip Size: 2.2 GB
Clips/three/FOLD_3.zip Size: 2.3 GB
Clips/three/FOLD_4.zip Size: 5.3 GB
Clips/three/FOLD_5.zip Size: 3.0 GB
Tracking
Content: Video frames and facial tracking points corresponding to each participant from each of the 120 clips
Format: zip archive containing jpeg images (frames) and pts text files (points)
Tracking/two/FOLD_1.zip Size: 756 MB
Tracking/two/FOLD_2.zip Size: 782 MB
Tracking/two/FOLD_3.zip Size: 881 MB
Tracking/two/FOLD_4.zip Size: 950 MB
Tracking/two/FOLD_5.zip Size: 1023 MB
Tracking/three/FOLD_1.zip Size: 1021 MB
Tracking/three/FOLD_2.zip Size: 542 MB
Tracking/three/FOLD_3.zip Size: 636 MB
Tracking/three/FOLD_4.zip Size: 716 MB
Tracking/three/FOLD_5.zip Size: 655 MB
SIFT
Content: SIFT features extracted from registered (aligned) images and facial points for each participant from each of the 120 clips
Format: zip archive containing mat files for SIFT features
SIFT/two/FOLD_1.zip Size: 1.70 GB
SIFT/two/FOLD_2.zip Size: 1.00 GB
SIFT/two/FOLD_3.zip Size: 0.97 GB
SIFT/two/FOLD_4.zip Size: 0.98 GB
SIFT/two/FOLD_5.zip Size: 0.97 GB
SIFT/three/FOLD_1.zip Size: 2.1 GB
SIFT/three/FOLD_2.zip Size: 1.4 GB
SIFT/three/FOLD_3.zip Size: 1.3 GB
SIFT/three/FOLD_4.zip Size: 1.4 GB
SIFT/three/FOLD_5.zip Size: 1.4 GB
AudioFeatures.zip
Content: Audio channel and low-level descriptor (LLD) acoustic features for each of the 120 clips
Format: zip archive containing wav recordings (audio channel) and mat files (features).
Size: 425 MB
Annotations.zip
Content: Frame-by-frame continuous-valued conflict intensity of the database clips corresponding to both the original annotations (provided by the 10 experts) and the single ground truth annotations (derived by us)
Format: zip archive containing mat files for both original and ground truth annotations
Size: 1.7 MB
The CONFER Database is provided for research purposes ONLY (NO commercial products).
Users of this data are kindly requested to cite the following paper:
C. Georgakis, Y. Panagakis, S. Zafeiriou, M. Pantic, “The Conflict Escalation Resolution (CONFER) Database”, in Image and Vision Computing, Special Issue on Multimodal Sentiment Analysis and Mining in the Wild Images, (accepted for publication). (pdf)
For any questions, please contact Christos Georgakis (christos.georgakis@imperial.ac.uk).
