Conditional Wasserstein Generative Adversarial Networks for Rebalancing Iris Image Datasets

Yung-Hui LI; Muhammad Saqlain ASLAM; Latifa Nabila HARFIYA; Ching-Chun CHANG

doi:10.1587/transinf.2021EDP7079

Conditional Wasserstein Generative Adversarial Networks for Rebalancing Iris Image Datasets

Yung-Hui LI, Muhammad Saqlain ASLAM, Latifa Nabila HARFIYA, Ching-Chun CHANG

Full Text Views

0

Cite this

Summary :

The recent development of deep learning-based generative models has sharply intensified the interest in data synthesis and its applications. Data synthesis takes on an added importance especially for some pattern recognition tasks in which some classes of data are rare and difficult to collect. In an iris dataset, for instance, the minority class samples include images of eyes with glasses, oversized or undersized pupils, misaligned iris locations, and iris occluded or contaminated by eyelids, eyelashes, or lighting reflections. Such class-imbalanced datasets often result in biased classification performance. Generative adversarial networks (GANs) are one of the most promising frameworks that learn to generate synthetic data through a two-player minimax game between a generator and a discriminator. In this paper, we utilized the state-of-the-art conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for generating the minority class of iris images which saves huge amount of cost of human labors for rare data collection. With our model, the researcher can generate as many iris images of rare cases as they want and it helps to develop any deep learning algorithm whenever large size of dataset is needed.

Publication: IEICE TRANSACTIONS on Information Vol.E104-D No.9 pp.1450-1458

Publication Date: 2021/09/01

Publicized: 2021/06/01

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2021EDP7079

Type of Manuscript: PAPER

Category: Artificial Intelligence, Data Mining

Authors

Yung-Hui LI
  National Central University,Hon Hai Research Institute
Muhammad Saqlain ASLAM
  National Central University
Latifa Nabila HARFIYA
  National Central University
Ching-Chun CHANG
  University of Warwick

Keyword

deep learning, generative adversarial network, iris image generation, machine learning neural networks, signal synthesis

Cite this

Copy

Yung-Hui LI, Muhammad Saqlain ASLAM, Latifa Nabila HARFIYA, Ching-Chun CHANG, "Conditional Wasserstein Generative Adversarial Networks for Rebalancing Iris Image Datasets" in IEICE TRANSACTIONS on Information, vol. E104-D, no. 9, pp. 1450-1458, September 2021, doi: 10.1587/transinf.2021EDP7079.
Abstract: The recent development of deep learning-based generative models has sharply intensified the interest in data synthesis and its applications. Data synthesis takes on an added importance especially for some pattern recognition tasks in which some classes of data are rare and difficult to collect. In an iris dataset, for instance, the minority class samples include images of eyes with glasses, oversized or undersized pupils, misaligned iris locations, and iris occluded or contaminated by eyelids, eyelashes, or lighting reflections. Such class-imbalanced datasets often result in biased classification performance. Generative adversarial networks (GANs) are one of the most promising frameworks that learn to generate synthetic data through a two-player minimax game between a generator and a discriminator. In this paper, we utilized the state-of-the-art conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for generating the minority class of iris images which saves huge amount of cost of human labors for rare data collection. With our model, the researcher can generate as many iris images of rare cases as they want and it helps to develop any deep learning algorithm whenever large size of dataset is needed.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7079/_p

Copy

@ARTICLE{e104-d_9_1450,
author={Yung-Hui LI, Muhammad Saqlain ASLAM, Latifa Nabila HARFIYA, Ching-Chun CHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Conditional Wasserstein Generative Adversarial Networks for Rebalancing Iris Image Datasets},
year={2021},
volume={E104-D},
number={9},
pages={1450-1458},
abstract={The recent development of deep learning-based generative models has sharply intensified the interest in data synthesis and its applications. Data synthesis takes on an added importance especially for some pattern recognition tasks in which some classes of data are rare and difficult to collect. In an iris dataset, for instance, the minority class samples include images of eyes with glasses, oversized or undersized pupils, misaligned iris locations, and iris occluded or contaminated by eyelids, eyelashes, or lighting reflections. Such class-imbalanced datasets often result in biased classification performance. Generative adversarial networks (GANs) are one of the most promising frameworks that learn to generate synthetic data through a two-player minimax game between a generator and a discriminator. In this paper, we utilized the state-of-the-art conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for generating the minority class of iris images which saves huge amount of cost of human labors for rare data collection. With our model, the researcher can generate as many iris images of rare cases as they want and it helps to develop any deep learning algorithm whenever large size of dataset is needed.},
keywords={},
doi={10.1587/transinf.2021EDP7079},
ISSN={1745-1361},
month={September},}

Copy

TY - JOUR
TI - Conditional Wasserstein Generative Adversarial Networks for Rebalancing Iris Image Datasets
T2 - IEICE TRANSACTIONS on Information
SP - 1450
EP - 1458
AU - Yung-Hui LI
AU - Muhammad Saqlain ASLAM
AU - Latifa Nabila HARFIYA
AU - Ching-Chun CHANG
PY - 2021
DO - 10.1587/transinf.2021EDP7079
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2021
AB - The recent development of deep learning-based generative models has sharply intensified the interest in data synthesis and its applications. Data synthesis takes on an added importance especially for some pattern recognition tasks in which some classes of data are rare and difficult to collect. In an iris dataset, for instance, the minority class samples include images of eyes with glasses, oversized or undersized pupils, misaligned iris locations, and iris occluded or contaminated by eyelids, eyelashes, or lighting reflections. Such class-imbalanced datasets often result in biased classification performance. Generative adversarial networks (GANs) are one of the most promising frameworks that learn to generate synthetic data through a two-player minimax game between a generator and a discriminator. In this paper, we utilized the state-of-the-art conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for generating the minority class of iris images which saves huge amount of cost of human labors for rare data collection. With our model, the researcher can generate as many iris images of rare cases as they want and it helps to develop any deep learning algorithm whenever large size of dataset is needed.
ER -