GANs and Artificial Facial Expressions in Synthetic Portraits

Rosado, Pilar; Fernandez, Ruben David; Reverter, Ferrán

doi:10.3390/bdcc5040063

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…Generative Adversarial Networks (GANs) [1,2,15] have achieved a series of impressive results in image-to-image translation tasks. This technique has widely spread to diverse domains such as art [19], medical research [20], and entertainment [21,22]. The inputs of the model are usually a face image and a set of control parameters.…”

Section: Facial Expression Manipulationmentioning

confidence: 99%

Combining GAN with reverse correlation to construct personalized facial expressions

Yan,

Soladié,

Aucouturier

et al. 2023

PLoS ONE

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Recent deep-learning techniques have made it possible to manipulate facial expressions in digital photographs or videos, however, these techniques still lack fine and personalized ways to control their creation. Moreover, current technologies are highly dependent on large labeled databases, which limits the range and complexity of expressions that can be modeled. Thus, these technologies cannot deal with non-basic emotions. In this paper, we propose a novel interdisciplinary approach combining the Generative Adversarial Network (GAN) with a technique inspired by cognitive sciences, psychophysical reverse correlation. Reverse correlation is a data-driven method able to extract an observer’s ‘mental representation’ of what a given facial expression should look like. Our approach can generate 1) personalized facial expression prototypes, 2) of basic emotions, and non-basic emotions that are not available in existing databases, and 3) without the need for expertise. Personalized prototypes obtained with reverse correlation can then be applied to manipulate facial expressions. In addition, our system challenges the universality of facial expression prototypes by proposing the concepts of dominant and complementary action units to describe facial expression prototypes. The evaluations we conducted on a limited number of emotions validate the effectiveness of our proposed method. The code is available at https://github.com/yansen0508/Mental-Deep-Reverse-Engineering.

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Section: Facial Expression Manipulationmentioning

confidence: 99%

Combining GAN with reverse correlation to construct personalized facial expressions

Yan,

Soladié,

Aucouturier

et al. 2023

PLoS ONE

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“…One of the most prevalent techniques for generating synthetic image data is the use of GANs [45,66,67]. These networks consist of two neural network models, the generator and the discriminator [68].…”

Section: Ruler Detection In Imagesmentioning

confidence: 99%

Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep Learning

Matuzevičius

2023

Electronics

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This research investigates the usefulness and efficacy of synthetic ruler images for the development of a deep learning-based ruler detection algorithm. Synthetic images offer a compelling alternative to real-world images as data sources in the development and advancement of computer vision systems. This research aims to answer whether using a synthetic dataset of ruler images is sufficient for training an effective ruler detector and to what extent such a detector could benefit from including synthetic images as a data source. The article presents the procedural method for generating synthetic ruler images, describes the methodology for evaluating the synthetic dataset using trained convolutional neural network (CNN)-based ruler detectors, and shares the compiled synthetic and real ruler image datasets. It was found that the synthetic dataset yielded superior results in training the ruler detectors compared with the real image dataset. The results support the utility of synthetic datasets as a viable and advantageous approach to training deep learning models, especially when real-world data collection presents significant logistical challenges. The evidence presented here strongly supports the idea that when carefully generated and used, synthetic data can effectively replace real images in the development of CNN-based detection systems.

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Understanding GANs: fundamentals, variants, training challenges, applications, and open problems

Ahmad,

Jaffri,

Chen

et al. 2024

Multimed Tools Appl

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GANs and Artificial Facial Expressions in Synthetic Portraits

Cited by 5 publications

References 14 publications

Combining GAN with reverse correlation to construct personalized facial expressions

Combining GAN with reverse correlation to construct personalized facial expressions

Rulers2023: An Annotated Dataset of Synthetic and Real Images for Ruler Detection Using Deep Learning

Understanding GANs: fundamentals, variants, training challenges, applications, and open problems

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