Search Results for author: Irene Kotsia

Found 12 papers, 6 papers with code

The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition

no code implementations29 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.

Action Unit Detection Arousal Estimation +1

Analysing Affective Behavior in the second ABAW2 Competition

no code implementations14 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.

Action Unit Detection Arousal Estimation

Fast-GANFIT: Generative Adversarial Network for High Fidelity 3D Face Reconstruction

1 code implementation16 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.

3D Face Reconstruction Generative Adversarial Network +1

Dense 3D Face Decoding over 2500FPS: Joint Texture & Shape Convolutional Mesh Decoders

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).

MeshGAN: Non-linear 3D Morphable Models of Faces

no code implementations25 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.

GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction

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)

3D Face Reconstruction Generative Adversarial Network +1

Deep Neural Network Augmentation: Generating Faces for Affect Analysis

no code implementations12 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 #7 on Facial Expression Recognition (FER) on RAF-DB (using extra training data)

Data Augmentation Face Generation +1

4DFAB: A Large Scale 4D Database for Facial Expression Analysis and Biometric Applications

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)

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

97 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)

Face Generation Face Identification +2

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