2 code implementations • 30 Mar 2021 • Adrian Bulat, Shiyang Cheng, Jing Yang, Andrew Garbett, Enrique Sanchez, Georgios Tzimiropoulos
Recent work on Deep Learning in the area of face analysis has focused on supervised learning for specific tasks of interest (e. g. face recognition, facial landmark localization etc.)
Ranked #1 on Facial Expression Recognition (FER) on BP4D
no code implementations • 14 Nov 2019 • Shiyang Cheng, Pingchuan Ma, Georgios Tzimiropoulos, Stavros Petridis, Adrian Bulat, Jie Shen, Maja Pantic
The proposed model significantly outperforms previous approaches on non-frontal views while retaining the superior performance on frontal and near frontal mouth views.
no code implementations • 11 Oct 2019 • Bingnan Luo, Jie Shen, Shiyang Cheng, Yujiang Wang, Maja Pantic
Specifically, we learn the shape prior from our dataset using VAE-GAN, and leverage the pre-trained encoder and discriminator to regularise the training of SegNet.
no code implementations • CVPR 2020 • Yujiang Wang, Mingzhi Dong, Jie Shen, Yang Wu, Shiyang Cheng, Maja Pantic
To the best of our knowledge, this is the first work to use reinforcement learning for online key-frame decision in dynamic video segmentation, and also the first work on its application on face videos.
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.
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 • 24 May 2018 • Yiming Lin, Shiyang Cheng, Jie Shen, Maja Pantic
36 state-of-the-art trackers, including facial landmark trackers, generic object trackers and trackers that we have fine-tuned or improved, are evaluated.
no code implementations • 20 Jan 2018 • Niannan Xue, Jiankang Deng, Shiyang Cheng, Yannis Panagakis, Stefanos Zafeiriou
Robust principal component analysis (RPCA) is a powerful method for learning low-rank feature representation of various visual data.
no code implementations • CVPR 2018 • Jiankang Deng, Shiyang Cheng, Niannan Xue, Yuxiang Zhou, Stefanos Zafeiriou
We demonstrate that by attaching the completed UV to the fitted mesh and generating instances of arbitrary poses, we can increase pose variations for training deep face recognition/verification models, and minimise pose discrepancy during testing, which lead to better performance.
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)
no code implementations • 28 Nov 2017 • Mengjiao Wang, Zhixin Shu, Shiyang Cheng, Yannis Panagakis, Dimitris Samaras, Stefanos Zafeiriou
Several factors contribute to the appearance of an object in a visual scene, including pose, illumination, and deformation, among others.
no code implementations • CVPR 2014 • Akshay Asthana, Stefanos Zafeiriou, Shiyang Cheng, Maja Pantic
We propose very efficient strategies to update the model and we show that is possible to automatically construct robust discriminative person and imaging condition specific models 'in-the-wild' that outperform state-of-the-art generic face alignment strategies.
no code implementations • CVPR 2013 • Akshay Asthana, Stefanos Zafeiriou, Shiyang Cheng, Maja Pantic
We present a novel discriminative regression based approach for the Constrained Local Models (CLMs) framework, referred to as the Discriminative Response Map Fitting (DRMF) method, which shows impressive performance in the generic face fitting scenario.