Conditional Wasserstein generative adversarial networks for rebalancing iris image datasets

Yung Hui Li, Muhammad Saqlain Aslam, Latifa Nabila Harfiya, Ching Chun Chang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)1450-1458
Number of pages9
JournalIEICE Transactions on Information and Systems
Issue number9
StatePublished - 2021


  • Deep learning
  • Generative adversarial network
  • Iris image generation
  • Machine learning neural networks
  • Signal synthesis


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