On the Feasibility of Creating Iris Periocular Morphed Images

• URL: http://arxiv.org/abs/2408.13496v1
• Date: Sat, 24 Aug 2024 06:48:46 GMT
• Title: On the Feasibility of Creating Iris Periocular Morphed Images
• Authors: Juan E. Tapia, Sebastian Gonzalez, Daniel Benalcazar, Christoph Busch,
• Abstract summary: This work proposes an end-to-end framework to produce iris morphs at the image level. It considers different stages such as pair subject selection, segmentation, morph creation, and a new iris recognition system. The results show that this approach obtained very realistic images that can confuse conventional iris recognition systems.
• Score: 9.021226651004055
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the last few years, face morphing has been shown to be a complex challenge for Face Recognition Systems (FRS). Thus, the evaluation of other biometric modalities such as fingerprint, iris, and others must be explored and evaluated to enhance biometric systems. This work proposes an end-to-end framework to produce iris morphs at the image level, creating morphs from Periocular iris images. This framework considers different stages such as pair subject selection, segmentation, morph creation, and a new iris recognition system. In order to create realistic morphed images, two approaches for subject selection are explored: random selection and similar radius size selection. A vulnerability analysis and a Single Morphing Attack Detection algorithm were also explored. The results show that this approach obtained very realistic images that can confuse conventional iris recognition systems.

Related papers

• 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)
• COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection [56.7599217711363]
  Face forgery recognition methods can only process one face at a time. Most face forgery recognition methods can only process one face at a time. We propose COMICS, an end-to-end framework for multi-face forgery detection.
  arXiv  Detail & Related papers  (2023-08-03T03:37:13Z)
• 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)
• Artificial Pupil Dilation for Data Augmentation in Iris Semantic Segmentation [0.0]
  Modern approaches to iris recognition utilize deep learning to segment the valid portion of the iris from the rest of the eye. This paper aims to improve the accuracy of iris semantic segmentation systems by introducing a novel data augmentation technique.
  arXiv  Detail & Related papers  (2022-12-24T13:31:56Z)
• Super-Resolution and Image Re-projection for Iris Recognition [67.42500312968455]
  Convolutional Neural Networks (CNNs) using different deep learning approaches attempt to recover realistic texture and fine grained details from low resolution images. In this work we explore the viability of these approaches for iris Super-Resolution (SR) in an iris recognition environment. Results show that CNNs and image re-projection can improve the results specially for the accuracy of recognition systems.
  arXiv  Detail & Related papers  (2022-10-20T09:46:23Z)
• Two-headed eye-segmentation approach for biometric identification [3.4998703934432682]
  This paper introduces the new two-headed architecture, where the eye components and eyelashes are segmented using two separate decoding modules. Thanks to the two-headed approach, we were also able to examine the quality of the model with the convex prior. We conducted an extensive evaluation of various learning scenarios on real-life conditions high-resolution near-infrared iris images.
  arXiv  Detail & Related papers  (2022-09-30T13:52:03Z)
• DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation [4.142375560633827]
  In dominant approaches to iris recognition, the size of a ring-shaped iris region is linearly scaled to a canonical rectangle. We propose a novel deep autoencoder-based model that can effectively learn complex movements of iris texture features directly from the data.
  arXiv  Detail & Related papers  (2022-07-18T23:23:23Z)
• Super-Resolution for Selfie Biometrics: Introduction and Application to Face and Iris [67.74999528342273]
  Lack of resolution has a negative impact on the performance of image-based biometrics. Super-resolution techniques have to be adapted for the particularities of images from a specific biometric modality. This chapter presents an overview of recent advances in super-resolution reconstruction of face and iris images.
  arXiv  Detail & Related papers  (2022-04-12T10:28:31Z)
• 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)
• Iris Recognition Based on SIFT Features [63.07521951102555]
  We use the Scale Invariant Feature Transformation (SIFT) for recognition using iris images. We extract characteristic SIFT feature points in scale space and perform matching based on the texture information around the feature points using the SIFT operator. We also show the complement between the SIFT approach and a popular matching approach based on transformation to polar coordinates and Log-Gabor wavelets.
  arXiv  Detail & Related papers  (2021-10-30T04:55:33Z)
• 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.