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George Trigeorgis

8 accepted papers

2022

MimicME: A Large Scale Diverse 4D Database for Facial Expression Analysis

Athanasios Papaioannou, Baris Gecer, Shiyang Cheng, Grigorios G. Chrysos, Jiankang Deng, Eftychia Fotiadou +12
ECCV 2022poster

"Recently, Deep Neural Networks (DNNs) have been shown to outperform traditional methods in many disciplines such as computer vision, speech recognition and natural language processing. A prerequisite for the successful application of DNNs is the big number of data. Even though various facial datase…

Cited by 13Source ↗PDF ↗code ↗Scholar ↗
2017

3D Face Morphable Models "In-The-Wild"

James Booth, Epameinondas Antonakos, Stylianos Ploumpis, George Trigeorgis, Yannis Panagakis, Stefanos Zafeiriou
CVPR 2017spotlight

3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have been collected containing both neutral as well as exp…

Cited by 213PDF ↗Scholar ↗
2017

DenseReg: Fully Convolutional Dense Shape Regression In-The-Wild

Riza Alp Guler, George Trigeorgis, Epameinondas Antonakos, Patrick Snape, Stefanos Zafeiriou, Iasonas Kokkinos
CVPR 2017poster

In this paper we propose to learn a mapping from image pixels into a dense template grid through a fully convolutional network. We formulate this task as a regression problem and train our network by leveraging upon manually annotated facial landmarks 'in-the-wild'. We use such landmarks to establ…

Cited by 239PDF ↗Scholar ↗
2017

Face Normals "In-The-Wild" Using Fully Convolutional Networks

George Trigeorgis, Patrick Snape, Iasonas Kokkinos, Stefanos Zafeiriou
CVPR 2017poster

In this work we pursue a data-driven approach to the problem of estimating surface normals from a single intensity image, focusing in particular on human faces. We introduce new methods to exploit the currently available facial databases for dataset construction and tailor a deep convolutional neura…

Cited by 58PDF ↗Scholar ↗
2016

Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network

George Trigeorgis, Fabien Ringeval, Raymond Brueckner, Erik Marchi, Mihalis A. Nicolaou, Björn W. Schuller +1
ICASSP 2016accepted

The automatic recognition of spontaneous emotions from speech is a challenging task. On the one hand, acoustic features need to be robust enough to capture the emotional content for various styles of speaking, and while on the other, machine learning algorithms need to be insensitive to outliers whi…

Cited by 0Source ↗Scholar ↗
2016

Domain Separation Networks

Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, Dumitru Erhan
NeurIPS 2016poster

The cost of large scale data collection and annotation often makes the application of machine learning algorithms to new tasks or datasets prohibitively expensive. One approach circumventing this cost is training models on synthetic data where annotations are provided automatically. Despite their ap…

Cited by 1882Source ↗PDF ↗code ↗Scholar ↗
2016

Mnemonic Descent Method: A Recurrent Process Applied for End-To-End Face Alignment

George Trigeorgis, Patrick Snape, Mihalis A. Nicolaou, Epameinondas Antonakos, Stefanos Zafeiriou
CVPR 2016poster

Cascaded regression has recently become the method of choice for solving non-linear least squares problems such as deformable image alignment. Given a sizeable training set, cascaded regression learns a set of generic rules that are sequentially applied to minimise the least squares problem. Despit…

Cited by 444PDF ↗Scholar ↗