Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis
Biometric systems based on iris recognition are currently being used in border control applications and mobile devices. However, research in iris recognition is stymied by various factors such as limited datasets of bonafide irides and presentation attack instruments; restricted intra-class variations; and privacy concerns. Some of these issues can be mitigated by the use of synthetic iris data. In this paper, we present a comprehensive review of state-of-the-art GAN-based synthetic iris image generation techniques, evaluating their strengths and limitations in producing realistic and useful iris images that can be used for both training and testing iris recognition systems and presentation attack detectors. In this regard, we first survey the various methods that have been used for synthetic iris generation and specifically consider generators based on StyleGAN, RaSGAN, CIT-GAN, iWarpGAN, StarGAN, etc. We then analyze the images generated by these models for realism, uniqueness, and biometric utility. This comprehensive analysis highlights the pros and cons of various GANs in the context of developing robust iris matchers and presentation attack detectors.
Code (0)
등록된 구현이 없습니다.
Tasks
Image GenerationIris RecognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Haven't I Seen You Before? Assessing Identity Leakage in Synthetic Irises
Generative Adversarial Networks (GANs) have proven to be a preferred method of synthesizing fake images of objects, such as faces, animals, and automobiles. It is not surprising these models can also generate ISO-complia…
Gradient-Guided Exploration of Generative Model's Latent Space for Controlled Iris Image Augmentations
Developing reliable iris recognition and presentation attack detection methods requires diverse datasets that capture realistic variations in iris features and a wide spectrum of anomalies. Because of the rich texture of…
Image AugmentationSynthetic Iris Presentation Attack using iDCGAN
Reliability and accuracy of iris biometric modality has prompted its large-scale deployment for critical applications such as border control and national ID projects. The extensive growth of iris recognition systems has …
Generative Adversarial NetworkIris RecognitionGenerative Iris Prior Embedded Transformer for Iris Restoration
Iris restoration from complexly degraded iris images, aiming to improve iris recognition performance, is a challenging problem. Due to the complex degradation, directly training a convolutional neural network (CNN) witho…
DecoderGenerative Adversarial NetworkIris RecognitionDeep GAN-Based Cross-Spectral Cross-Resolution Iris Recognition
In recent years, cross-spectral iris recognition has emerged as a promising biometric approach to establish the identity of individuals. However, matching iris images acquired at different spectral bands (i.e., matching …
Generative Adversarial NetworkIris Recognition