755 citations · 4.3k across the 133 of their papers we have counts for
16 papers · 1 filter
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
Jia Guo, Jiankang Deng, Niannan Xue +1
Facial landmark localisation in images captured in-the-wild is an important and challenging problem. The current state-of-the-art revolves around certain kinds of Deep Convolutiona…
Photorealistic Facial Synthesis in the Dimensional Affect Space
Dimitrios Kollias, Shiyang Cheng, Maja Pantic +1
This paper presents a novel approach for synthesizing facial affect, which is based on our annotating 600,000 frames of the 4DFAB database in terms of valence and arousal. The inpu…
A Multi-Task Learning & Generation Framework: Valence-Arousal, Action Units & Primary Expressions
Dimitrios Kollias, Stefanos Zafeiriou
Over the past few years many research efforts have been devoted to the field of affect analysis. Various approaches have been proposed for: i) discrete emotion recognition in terms…
Aff-Wild2: Extending the Aff-Wild Database for Affect Recognition
Dimitrios Kollias, Stefanos Zafeiriou
Automatic understanding of human affect using visual signals is a problem that has attracted significant interest over the past 20 years. However, human emotional states are quite…
Deep Neural Network Augmentation: Generating Faces for Affect Analysis
Dimitrios Kollias, Shiyang Cheng, Evangelos Ververas +2
This paper presents a novel approach for synthesizing facial affect; either in terms of the six basic expressions (i.e., anger, disgust, fear, joy, sadness and surprise), or in ter…
Training Deep Neural Networks with Different Datasets In-the-wild: The Emotion Recognition Paradigm
Dimitrios Kollias, Stefanos Zafeiriou
A novel procedure is presented in this paper, for training a deep convolutional and recurrent neural network, taking into account both the available training data set and some info…
Robust Conditional Generative Adversarial Networks
Grigorios G. Chrysos, Jean Kossaifi, Stefanos Zafeiriou
Conditional generative adversarial networks (cGAN) have led to large improvements in the task of conditional image generation, which lies at the heart of computer vision. The major…
A Multi-component CNN-RNN Approach for Dimensional Emotion Recognition in-the-wild
Dimitrios Kollias, Stefanos Zafeiriou
This paper presents our approach to the One-Minute Gradual-Emotion Recognition (OMG-Emotion) Challenge, focusing on dimensional emotion recognition through visual analysis of the p…
Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond
Dimitrios Kollias, Panagiotis Tzirakis, Mihalis A. Nicolaou +5
Automatic understanding of human affect using visual signals is of great importance in everyday human-machine interactions. Appraising human emotional states, behaviors and reactio…
DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild
Riza Alp Guler, Yuxiang Zhou, George Trigeorgis +4
In this work we use deep learning to establish dense correspondences between a 3D object model and an image "in the wild". We introduce "DenseReg", a fully-convolutional neural net…
Motion deblurring of faces
Grigorios G. Chrysos, Paolo Favaro, Stefanos Zafeiriou
Face analysis is a core part of computer vision, in which remarkable progress has been observed in the past decades. Current methods achieve recognition and tracking with invarianc…
End2You -- The Imperial Toolkit for Multimodal Profiling by End-to-End Learning
Panagiotis Tzirakis, Stefanos Zafeiriou, Bjorn W. Schuller
We introduce End2You -- the Imperial College London toolkit for multimodal profiling by end-to-end deep learning. End2You is an open-source toolkit implemented in Python and is bas…
Side Information for Face Completion: a Robust PCA Approach
Niannan Xue, Jiankang Deng, Shiyang Cheng +2
Robust principal component analysis (RPCA) is a powerful method for learning low-rank feature representation of various visual data. However, for certain types as well as significa…
Visual Data Augmentation through Learning
Grigorios G. Chrysos, Yannis Panagakis, Stefanos Zafeiriou
The rapid progress in machine learning methods has been empowered by i) huge datasets that have been collected and annotated, ii) improved engineering (e.g. data pre-processing/nor…
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Jiankang Deng, Jia Guo, Jing Yang +3
Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability. In this paper, we first i…
Robust Kronecker Component Analysis
Mehdi Bahri, Yannis Panagakis, Stefanos Zafeiriou
Dictionary learning and component analysis models are fundamental for learning compact representations that are relevant to a given task (feature extraction, dimensionality reducti…