A Survey on Face Data Augmentation

Wang, Xiang; Wang, Kai; Lian, Shiguo

doi:10.48550/arxiv.1904.11685

Cited by 7 publications

(7 citation statements)

References 126 publications

(248 reference statements)

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“…Through this work, we hope to emulate past papers which have surveyed DA methods for other types of data, such as images (Shorten and Khoshgoftaar, 2019), faces (Wang et al, 2019b), and time series (Iwana and Uchida, 2020). We hope to draw further attention, elicit broader interest, and motivate additional work in DA, particularly for NLP.…”

Section: Arxiv:210503075v3 [Cscl] 29 May 2021mentioning

confidence: 92%

A Survey of Data Augmentation Approaches for NLP

Feng

Gangal

Wei

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

297

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Data augmentation has recently seen increased interest in NLP due to more work in lowresource domains, new tasks, and the popularity of large-scale neural networks that require large amounts of training data. Despite this recent upsurge, this area is still relatively underexplored, perhaps due to the challenges posed by the discrete nature of language data. In this paper, we present a comprehensive and unifying survey of data augmentation for NLP by summarizing the literature in a structured manner. We first introduce and motivate data augmentation for NLP, and then discuss major methodologically representative approaches. Next, we highlight techniques that are used for popular NLP applications and tasks. We conclude by outlining current challenges and directions for future research. Overall, our paper aims to clarify the landscape of existing literature in data augmentation for NLP and motivate additional work in this area. We also present a GitHub repository with a paper list that will be continuously updated at https://github.com/styfeng/DataAug4NLP.

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Section: Arxiv:210503075v3 [Cscl] 29 May 2021mentioning

confidence: 92%

A Survey of Data Augmentation Approaches for NLP

Feng

Gangal

Wei

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

297

View full text Add to dashboard Cite

show abstract

“…It can help in enhancing the generalization ability of the network and prevent overfitting. Data augmentation can increase facial recognition systems' accuracy by training them on the original data set images and the images that were modified using data augmentation techniques [24,25]. In this paper, five types of data augmentation techniques have been used: flip [26], brightness [27], crop [28], rotate [29], and Impulse Noise [30], as shown in Fig.…”

Section: Data Augmentationmentioning

confidence: 99%

Generating and Improving a Dataset of Masked Faces Using Data Augmentation

Waleed Ayad,

Siraj Qays,

Ali Al-Naji

2023

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Before the spread of the COVID-19 virus in 2020, modern face recognition systems performed excellently, but then the wearing of masks was imposed by countries on their population, which led to a noteworthy decrease in the discriminatory ability of those systems, where they had been trained on large-scale datasets of unmasked faces and not available large-scale masked faces datasets that time. To contribute to addressing the shortage of large-scale data sets that consist of people wearing masks, a developed method has been presented to create simulated masks and overlay them on faces in two main steps. The first step was to detect, align and crop the faces of unmasked faces datasets in a dataset and then apply simulated masks on the faces utilizing the dlib-ml library. This method was used to generate a dataset for masked faces (CASIA-mask). The second step used five techniques of data augmentation with the generated dataset. To evaluate the masked dataset and data augmentation, an accuracy of 96.4% was achieved by training one of the latest and most important facial recognition systems, FaceNet, on the masked dataset. The same system also achieved excellent results of 97.71% when trained on CASIA-mask and data augmentation together.

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“…Where the oversampling is entailed, these methods oftentimes rely on Synthetic Minority Oversampling Technique (SMOTE) [33] -an established geometric approach to random oversampling and data augmentation in Euclidean space, followed by many extensions [37], [38]. For faces, the survey [39] covers a large set of augmentation methods, ranging from attribute transformations [40] to generative methods (e.g., GAN-based [1], [41]). Other modern augmentation methods include VAEs [42], [43] and NFs [44].…”

Section: Related Workmentioning

confidence: 99%

LAMBO: Landmarks Augmentation With Manifold-Barycentric Oversampling

et al. 2022

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We propose the first data augmentation method based on optimal transport theory, with the generated data being guaranteed to belong to the original data manifold. The proposed algorithm randomly samples a clique in the nearest-neighbors graph representing the data knowledge and computes the Wasserstein barycenter between the neighbours with random uniform weights. Being extremely naturallooking, many such barycenters are then produced iteratively to overpopulate the original dataset. We apply this approach to the problem of landmarks detection in unsupervised and semi-supervised scenarios in the popular tasks of face keypoints extraction, pose detection, and the segmentation of anatomical contours in medical imaging. The barycentric oversampling approach is shown to outperform state-of-the-art data augmentation methods. The code is available at https://github.com/cviaai/LAMBO/.

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A Survey on Face Data Augmentation

Cited by 7 publications

References 126 publications

A Survey of Data Augmentation Approaches for NLP

A Survey of Data Augmentation Approaches for NLP

Generating and Improving a Dataset of Masked Faces Using Data Augmentation

LAMBO: Landmarks Augmentation With Manifold-Barycentric Oversampling

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