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Found 25 papers, 6 papers with code
REACT 2024: the Second Multiple Appropriate Facial Reaction Generation Challenge
In dyadic interactions, humans communicate their intentions and state of mind using verbal and non-verbal cues, where multiple different facial reactions might be appropriate in response to a specific speaker behaviour.
REACT2023: the first Multi-modal Multiple Appropriate Facial Reaction Generation Challenge
The Multi-modal Multiple Appropriate Facial Reaction Generation Challenge (REACT2023) is the first competition event focused on evaluating multimedia processing and machine learning techniques for generating human-appropriate facial reactions in various dyadic interaction scenarios, with all participants competing strictly under the same conditions.
Are 3D Face Shapes Expressive Enough for Recognising Continuous Emotions and Action Unit Intensities?
We also study how 3D face shapes performed on AU intensity estimation on BP4D and DISFA datasets, and report that 3D face features were on par with 2D appearance features in AUs 4, 6, 10, 12, and 25, but not the entire set of AUs.
COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition
In particular, we impose Calibration and Ordinal Ranking constraints on the variance vectors of audiovisual latent distributions.
Continuous-Time Audiovisual Fusion with Recurrence vs. Attention for In-The-Wild Affect Recognition
To clearly understand the performance differences between recurrent and attention models in audiovisual affect recognition, we present a comprehensive evaluation of fusion models based on LSTM-RNNs, self-attention and cross-modal attention, trained for valence and arousal estimation.
Two-stage Temporal Modelling Framework for Video-based Depression Recognition using Graph Representation
The short-term depressive behaviour modelling stage first deep learns depression-related facial behavioural features from multiple short temporal scales, where a Depression Feature Enhancement (DFE) module is proposed to enhance the depression-related clues for all temporal scales and remove non-depression noises.
Learning Graph Representation of Person-specific Cognitive Processes from Audio-visual Behaviours for Automatic Personality Recognition
This approach builds on two following findings in cognitive science: (i) human cognition partially determines expressed behaviour and is directly linked to true personality traits; and (ii) in dyadic interactions individuals' nonverbal behaviours are influenced by their conversational partner behaviours.
Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition
Temporal context is key to the recognition of expressions of emotion.
A Transfer Learning approach to Heatmap Regression for Action Unit intensity estimation
Action Units (AUs) are geometrically-based atomic facial muscle movements known to produce appearance changes at specific facial locations.
A recurrent cycle consistency loss for progressive face-to-face synthesis
To the best of our knowledge, we are the first to propose a loss to overcome the limitation of the cycle consistency loss, and the first to propose an ''in-the-wild'' landmark guided synthesis approach.
EMOPAIN Challenge 2020: Multimodal Pain Evaluation from Facial and Bodily Expressions
The EmoPain 2020 Challenge is the first international competition aimed at creating a uniform platform for the comparison of machine learning and multimedia processing methods of automatic chronic pain assessment from human expressive behaviour, and also the identification of pain-related behaviours.
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition
The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions.
Inferring Dynamic Representations of Facial Actions from a Still Image
We present a novel approach to capture multiple scales of such temporal dynamics, with an application to facial Action Unit (AU) intensity estimation and dimensional affect estimation.
Triple consistency loss for pairing distributions in GAN-based face synthesis
To show this is effective, we incorporate the triple consistency loss into the training of a new landmark-guided face to face synthesis, where, contrary to previous works, the generated images can simultaneously undergo a large transformation in both expression and pose.
Joint Action Unit localisation and intensity estimation through heatmap regression
Contrary to previous works that try to learn an unsupervised representation of the Action Unit regions, we propose to directly and jointly estimate all AU intensities through heatmap regression, along with the location in the face where they cause visible changes.
Noise Invariant Frame Selection: A Simple Method to Address the Background Noise Problem for Text-independent Speaker Verification
The performance of speaker-related systems usually degrades heavily in practical applications largely due to the presence of background noise.
Fusing Deep Learned and Hand-Crafted Features of Appearance, Shape, and Dynamics for Automatic Pain Estimation
Automatic continuous time, continuous value assessment of a patient's pain from face video is highly sought after by the medical profession.
ChaLearn Looking at People and Faces of the World: Face Analysis Workshop and Challenge 2016
A custom-build application was used to collect and label data about the apparent age of people (as opposed to the real age).
Ranked #2 on
Gender Prediction
on FotW Gender
A Functional Regression approach to Facial Landmark Tracking
This paper presents a Functional Regression solution to the least squares problem, which we coin Continuous Regression, resulting in the first real-time incremental face tracker.
Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data
In this work, we present a novel methodology to aid diagnostic predictions about the presence/absence of ADHD and ASD by automatic visual analysis of a person's behaviour.
A CNN Cascade for Landmark Guided Semantic Part Segmentation
This paper proposes a CNN cascade for semantic part segmentation guided by pose-specific information encoded in terms of a set of landmarks (or keypoints).
Cascaded Continuous Regression for Real-time Incremental Face Tracking
We then derive the incremental learning updates for CCR (iCCR) and show that it is an order of magnitude faster than standard incremental learning for cascaded regression, bringing the time required for the update from seconds down to a fraction of a second, thus enabling real-time tracking.
AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and Challenge
The Audio/Visual Emotion Challenge and Workshop (AVEC 2016) "Depression, Mood and Emotion" will be the sixth competition event aimed at comparison of multimedia processing and machine learning methods for automatic audio, visual and physiological depression and emotion analysis, with all participants competing under strictly the same conditions.
TRIC-track: Tracking by Regression With Incrementally Learned Cascades
This paper proposes a novel approach to part-based tracking by replacing local matching of an appearance model by direct prediction of the displacement between local image patches and part locations.
Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit Detection
We thus consider a novel problem: all AU models for the target subject are to be learnt using person-specific annotated data for a reference AU (AU12 in our case), and no data or little data regarding the target AU.
