Generating photo-realistic training data to improve face recognition accuracy

Trigueros, Daniel Sáez; Li, Meng; Hartnett, Margaret

doi:10.1016/j.neunet.2020.11.008

Cited by 28 publications

(21 citation statements)

References 38 publications

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“…Apart from medical imaging applications, GANs have been also recently used to generate photorealistic images to enhance the training data sets for face recognition applications [TMH18, GBKK18]. Finally, Kar et al .…”

Section: Image Synthesis Methods Overviewmentioning

confidence: 99%

A Survey of Image Synthesis Methods for Visual Machine Learning

Tsirikoglou

Eilertsen

Unger

2020

Computer Graphics Forum

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Image synthesis designed for machine learning applications provides the means to efficiently generate large quantities of training data while controlling the generation process to provide the best distribution and content variety. With the demands of deep learning applications, synthetic data have the potential of becoming a vital component in the training pipeline. Over the last decade, a wide variety of training data generation methods has been demonstrated. The potential of future development calls to bring these together for comparison and categorization. This survey provides a comprehensive list of the existing image synthesis methods for visual machine learning. These are categorized in the context of image generation, using a taxonomy based on modelling and rendering, while a classification is also made concerning the computer vision applications they are used. We focus on the computer graphics aspects of the methods, to promote future image generation for machine learning. Finally, each method is assessed in terms of quality and reported performance, providing a hint on its expected learning potential. The report serves as a comprehensive reference, targeting both groups of the applications and data development sides. A list of all methods and papers reviewed herein can be found at https://computergraphics.on.liu.se/image_synthesis_methods_for_visual_machine_learning/.

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Section: Image Synthesis Methods Overviewmentioning

confidence: 99%

A Survey of Image Synthesis Methods for Visual Machine Learning

Tsirikoglou

Eilertsen

Unger

2020

Computer Graphics Forum

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“…However, some recognition applications have benefited, related to medical analysis [52] that usually use abstract conditions such as segmentation masks [221]. Among them, a few examples tackle face recognition [74,247] and privacy-related issues [246].…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Synthetic data for visual machine learning : A data-centric approach

Tsirikoglou

2022

Linköping Studies in Science and Technology. Dissertations

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Deep learning allows computers to learn from observations, or else training data. Successful application development requires skills in neural network design, adequate computational resources, and a training data distribution that covers the application domain. We are currently witnessing an artificial intelligence (AI) outbreak with enough computational power to train very deep networks and build models that achieve similar or better than human performance. The crucial factor for the algorithms to succeed has proven to be the training data fed to the learning process. Too little or low quality or out-of-the-target distribution data will lead to poorly performing models no matter the capacity and the data regularization methods.This thesis is a data-centric approach to AI and presents a set of contributions related to synthesizing images for training supervised visual machine learning. It is motivated by the profound potential of synthetic data in cases of low availability of captured data, expensive acquisition and annotation, and privacy and ethical issues. The presented work aims to generate images similar to samples drawn from the target distribution and evaluate the generated data as the sole training data source and in conjunction with captured imagery. For this, two synthesis methods are explored: computer graphics and generative modeling. Computer graphics-based generation methods and synthetic datasets for computer vision tasks are thoroughly reviewed. In the same context, a system employing procedural modeling and physically-based rendering is introduced for data generation for urban scene understanding. The scheme is flexible, easily scalable, and produces complex and diverse images with pixel-perfect annotations at no cost. Generative Adversarial Networks (GANs) are also used to generate images for small data scenarios augmentation. The strategy advances the model's performance and robustness. Finally, ensembles of independently trained GANs investigate ways to improve images' diversity and create synthetic data to serve as the only training source.The application areas of the presented contributions relate to two image modalities, natural and histopathology images, to cover different aspects in the generation methods and the tasks' characteristics and requirements. There are showcased synthesized examples of natural images for automotive applications and weather classification, and histopathology images for breast cancer and colon adenocarcinoma metastasis detection. This thesis, as a whole, promotes data-centric supervised deep learning development by highlighting the potential of synthetic data as a training data resource. It emphasizes the control over the formation process, the ability of multi-modality formats, and the automatic generation of annotations.

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“…e study in [45] shows a case of generating a photo of a face, and the photo is very realistic. erefore, the paper has attracted wide attention from the media.…”

Section: Face Applications With Ganmentioning

confidence: 99%

FRGAN: A Blind Face Restoration with Generative Adversarial Networks

Wei

Li²,

Chen³

et al. 2021

Mathematical Problems in Engineering

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Recent works based on deep learning and facial priors have performed well in superresolving severely degraded facial images. However, due to the limitation of illumination, pixels of the monitoring probe itself, focusing area, and human motion, the face image is usually blurred or even deformed. To address this problem, we properly propose Face Restoration Generative Adversarial Networks to improve the resolution and restore the details of the blurred face. They include the Head Pose Estimation Network, Postural Transformer Network, and Face Generative Adversarial Networks. In this paper, we employ the following: (i) Swish-B activation function that is used in Face Generative Adversarial Networks to accelerate the convergence speed of the cross-entropy cost function, (ii) a special prejudgment monitor that is added to improve the accuracy of the discriminant, and (iii) the modified Postural Transformer Network that is used with 3D face reconstruction network to correct faces at different expression pose angles. Our method improves the resolution of face image and performs well in image restoration. We demonstrate how our method can produce high-quality faces, and it is superior to the most advanced methods on the reconstruction task of blind faces for in-the-wild images; especially, our 8 × SR SSIM and PSNR are, respectively, 0.078 and 1.16 higher than FSRNet in AFLW.

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Generating photo-realistic training data to improve face recognition accuracy

Cited by 28 publications

References 38 publications

A Survey of Image Synthesis Methods for Visual Machine Learning

A Survey of Image Synthesis Methods for Visual Machine Learning

Synthetic data for visual machine learning : A data-centric approach

FRGAN: A Blind Face Restoration with Generative Adversarial Networks

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