no code implementations • 29 Feb 2024 • Dimitrios Kollias, Panagiotis Tzirakis, Alan Cowen, Stefanos Zafeiriou, Irene Kotsia, Alice Baird, Chris Gagne, Chunchang Shao, Guanyu Hu
This paper describes the 6th Affective Behavior Analysis in-the-wild (ABAW) Competition, which is part of the respective Workshop held in conjunction with IEEE CVPR 2024.
no code implementations • 14 Jun 2021 • Dimitrios Kollias, Irene Kotsia, Elnar Hajiyev, Stefanos Zafeiriou
The Affective Behavior Analysis in-the-wild (ABAW2) 2021 Competition is the second -- following the first very successful ABAW Competition held in conjunction with IEEE FG 2020- Competition that aims at automatically analyzing affect.
1 code implementation • 16 May 2021 • Baris Gecer, Stylianos Ploumpis, Irene Kotsia, Stefanos Zafeiriou
In this paper, we take a radically different approach and harness the power of Generative Adversarial Networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images.
74 code implementations • 2 May 2019 • Jiankang Deng, Jia Guo, Yuxiang Zhou, Jinke Yu, Irene Kotsia, Stefanos Zafeiriou
Face Analysis Project on MXNet
Ranked #3 on Face Detection on WIDER Face (Medium)
no code implementations • CVPR 2019 • Yuxiang Zhou, Jiankang Deng, Irene Kotsia, Stefanos Zafeiriou
3D Morphable Models (3DMMs) are statistical models that represent facial texture and shape variations using a set of linear bases and more particular Principal Component Analysis (PCA).
no code implementations • 25 Mar 2019 • Shiyang Cheng, Michael Bronstein, Yuxiang Zhou, Irene Kotsia, Maja Pantic, Stefanos Zafeiriou
Generative Adversarial Networks (GANs) are currently the method of choice for generating visual data.
1 code implementation • CVPR 2019 • Baris Gecer, Stylianos Ploumpis, Irene Kotsia, Stefanos Zafeiriou
In this paper, we take a radically different approach and harness the power of Generative Adversarial Networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images.
Ranked #1 on 3D Face Reconstruction on Florence (Average 3D Error metric)
no code implementations • 12 Nov 2018 • Dimitrios Kollias, Shiyang Cheng, Evangelos Ververas, Irene Kotsia, Stefanos Zafeiriou
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 terms of valence (i. e., how positive or negative is an emotion) and arousal (i. e., power of the emotion activation).
Ranked #6 on Facial Expression Recognition (FER) on RAF-DB (Avg. Accuracy metric, using extra training data)
no code implementations • CVPR 2018 • Shiyang Cheng, Irene Kotsia, Maja Pantic, Stefanos Zafeiriou
The progress we are currently witnessing in many computer vision applications, including automatic face analysis, would not be made possible without tremendous efforts in collecting and annotating large scale visual databases.
Facial Expression Recognition Facial Expression Recognition (FER)
1 code implementation • 29 Apr 2018 • Dimitrios Kollias, Panagiotis Tzirakis, Mihalis A. Nicolaou, Athanasios Papaioannou, Guoying Zhao, Björn Schuller, Irene Kotsia, Stefanos Zafeiriou
Automatic understanding of human affect using visual signals is of great importance in everyday human-machine interactions.
101 code implementations • CVPR 2019 • Jiankang Deng, Jia Guo, Jing Yang, Niannan Xue, Irene Kotsia, Stefanos Zafeiriou
Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability.
Ranked #1 on Face Verification on Labeled Faces in the Wild (using extra training data)
1 code implementation • 5 Dec 2017 • Shiyang Cheng, Irene Kotsia, Maja Pantic, Stefanos Zafeiriou
4DFAB contains recordings of 180 subjects captured in four different sessions spanning over a five-year period.
Facial Expression Recognition Facial Expression Recognition (FER)