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)
Facial tracker (2011)
Active Appearance Model-based Facial-point Tracker (AAM-FPT): The AAM-FPT can be used to track 40 characteristic facial points shown in the Figure below. AAM-FPT consists of a hierarchy of three Active Appearance Models (AAM) and estimates the movements of the Head, Face, Eyebrows, Lips, Eyelids and Irises in 3D. Each AAM combines robust stochastic and deterministic appearance modelling with an optimized Levenberg-Marquardt Algorithm (LMA). The tracker has been implemented as a hierarchy of AAMs to enable handling self-occlusions, rigid and non-rigid motion. The powerful feature of the AAM-FPT is that it does not require prior training since it learns the appearance models on-line, while modelling outliers and dealing with illumination changes, occlusions, scale variation, out-of-plane movements, and translucent textures such as eyeglasses and sunglasses. The AAM-FPT is initialised automatically when a near frontal head pose is detected, using Active Shape Models (ASM) face alignment proposed in (Milborrow & Nicolls, 2008). The target of the initialization is to estimate the Deformation and Animation modes (SMD and AMD), which fit a Candide wireframe to the face within the face region detected by the face-detection module. The SMD and AMD parameters have been learned off-line from tracked sequences extracted from various public databases made available for research on automatic facial expression recognition.