Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis

• URL: http://arxiv.org/abs/2404.17105v2
• Date: Sat, 11 May 2024 21:36:17 GMT
• Title: Synthesizing Iris Images using Generative Adversarial Networks: Survey and Comparative Analysis
• Authors: Shivangi Yadav, Arun Ross,
• Abstract summary: We present a review of state-of-the-art GAN-based synthetic iris image generation techniques. 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.
• Score: 11.5164036021499
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Privacy-Safe Iris Presentation Attack Detection [4.215251065887862]
  This paper proposes a framework for a privacy-safe iris presentation attack detection (PAD) method. It is designed solely with synthetically-generated, identity-leakage-free iris images. The method is evaluated in a classical way using state-of-the-art iris PAD benchmarks.
  arXiv  Detail & Related papers  (2024-08-05T18:09:02Z)
• GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning [50.7702397913573]
  The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology. We propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection.
  arXiv  Detail & Related papers  (2024-02-03T03:13:50Z)
• EyePreserve: Identity-Preserving Iris Synthesis [8.973296574093506]
  This paper presents the first method of fully data-driven, identity-preserving, pupil size-varying synthesis of iris images. Two immediate applications of the proposed approach are: (a) synthesis of, or enhancement of the existing biometric datasets for iris recognition, and (b) helping forensic human experts in examining iris image pairs with significant differences in pupil dilation.
  arXiv  Detail & Related papers  (2023-12-19T10:29:29Z)
• Periocular biometrics: databases, algorithms and directions [69.35569554213679]
  Periocular biometrics has been established as an independent modality due to concerns on the performance of iris or face systems in uncontrolled conditions. This paper presents a review of the state of the art in periocular biometric research.
  arXiv  Detail & Related papers  (2023-07-26T11:14:36Z)
• Haven't I Seen You Before? Assessing Identity Leakage in Synthetic Irises [4.142375560633827]
  This paper presents analysis for three different iris matchers at varying points in the GAN training process to diagnose where and when authentic training samples are in jeopardy of leaking through the generative process. Our results show that while most synthetic samples do not show signs of identity leakage, a handful of generated samples match authentic (training) samples nearly perfectly, with consensus across all matchers.
  arXiv  Detail & Related papers  (2022-11-03T00:34:47Z)
• Direct attacks using fake images in iris verification [59.68607707427014]
  A database of fake iris images has been created from real iris of the BioSec baseline database. We show that the system is vulnerable to direct attacks, pointing out the importance of having countermeasures.
  arXiv  Detail & Related papers  (2021-10-30T05:01:06Z)
• Toward Accurate and Reliable Iris Segmentation Using Uncertainty Learning [96.72850130126294]
  We propose an Iris U-transformer (IrisUsformer) for accurate and reliable iris segmentation. For better accuracy, we elaborately design IrisUsformer by adopting position-sensitive operation and re-packaging transformer block. We show that IrisUsformer achieves better segmentation accuracy using 35% MACs of the SOTA IrisParseNet.
  arXiv  Detail & Related papers  (2021-10-20T01:37:19Z)
• Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition [61.87842307164351]
  We first propose an Identity-Aware CycleGAN (IACycleGAN) model that applies a new perceptual loss to supervise the image generation network. It improves CycleGAN on photo-sketch synthesis by paying more attention to the synthesis of key facial regions, such as eyes and nose. We develop a mutual optimization procedure between the synthesis model and the recognition model, which iteratively synthesizes better images by IACycleGAN.
  arXiv  Detail & Related papers  (2021-03-30T01:30:08Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)
• SIP-SegNet: A Deep Convolutional Encoder-Decoder Network for Joint Semantic Segmentation and Extraction of Sclera, Iris and Pupil based on Periocular Region Suppression [8.64118000141143]
  multimodal biometric recognition systems have the ability to deal with the limitations of unimodal biometric systems. Such systems possess high distinctiveness, permanence, and performance while, technologies based on other biometric traits can be easily compromised. This work presents a novel deep learning framework called SIP-SegNet, which performs the joint semantic segmentation of ocular traits.
  arXiv  Detail & Related papers  (2020-02-15T15:20:44Z)
• Segmentation-Aware and Adaptive Iris Recognition [24.125681602124477]
  The quality of iris images acquired at-a-distance or under less constrained imaging environments is known to degrade the iris matching accuracy. The periocular information is inherently embedded in such iris images and can be exploited to assist in the iris recognition under such non-ideal scenarios. This paper presents such a segmentation-assisted adaptive framework for more accurate less-constrained iris recognition.
  arXiv  Detail & Related papers  (2019-12-31T04:31:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.