DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation
- URL: http://arxiv.org/abs/2207.08980v1
- Date: Mon, 18 Jul 2022 23:23:23 GMT
- Title: DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation
- Authors: Siamul Karim Khan, Patrick Tinsley and Adam Czajka
- Abstract summary: 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.
- Score: 4.142375560633827
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Nonlinear iris texture deformations due to pupil size variations are one of
the main factors responsible for within-class variance of genuine comparison
scores in iris recognition. In dominant approaches to iris recognition, the
size of a ring-shaped iris region is linearly scaled to a canonical rectangle,
used further in encoding and matching. However, the biological complexity of
iris sphincter and dilator muscles causes the movements of iris features to be
nonlinear in a function of pupil size, and not solely organized along radial
paths. Alternatively to the existing theoretical models based on biomechanics
of iris musculature, in this paper we propose a novel deep autoencoder-based
model that can effectively learn complex movements of iris texture features
directly from the data. The proposed model takes two inputs, (a) an
ISO-compliant near-infrared iris image with initial pupil size, and (b) the
binary mask defining the target shape of the iris. The model makes all the
necessary nonlinear deformations to the iris texture to match the shape of iris
in image (a) with the shape provided by the target mask (b). The
identity-preservation component of the loss function helps the model in finding
deformations that preserve identity and not only visual realism of generated
samples. We also demonstrate two immediate applications of this model: better
compensation for iris texture deformations in iris recognition algorithms,
compared to linear models, and creation of generative algorithm that can aid
human forensic examiners, who may need to compare iris images with large
difference in pupil dilation. We offer the source codes and model weights
available along with this paper.
Related papers
- Progressive Retinal Image Registration via Global and Local Deformable Transformations [49.032894312826244]
We propose a hybrid registration framework called HybridRetina.
We use a keypoint detector and a deformation network called GAMorph to estimate the global transformation and local deformable transformation.
Experiments on two widely-used datasets, FIRE and FLoRI21, show that our proposed HybridRetina significantly outperforms some state-of-the-art methods.
arXiv Detail & Related papers (2024-09-02T08:43:50Z) - On the Feasibility of Creating Iris Periocular Morphed Images [9.021226651004055]
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.
arXiv Detail & Related papers (2024-08-24T06:48:46Z) - 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) - iWarpGAN: Disentangling Identity and Style to Generate Synthetic Iris
Images [13.60510525958336]
iWarpGAN generates iris images with both inter- and intra-class variations.
The utility of the synthetically generated images is demonstrated by improving the performance of deep learning based iris matchers.
arXiv Detail & Related papers (2023-05-21T23:10:14Z) - Shape-Erased Feature Learning for Visible-Infrared Person
Re-Identification [90.39454748065558]
Body shape is one of the significant modality-shared cues for VI-ReID.
We propose shape-erased feature learning paradigm that decorrelates modality-shared features in two subspaces.
Experiments on SYSU-MM01, RegDB, and HITSZ-VCM datasets demonstrate the effectiveness of our method.
arXiv Detail & Related papers (2023-04-09T10:22:10Z) - 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) - 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) - SIR: Self-supervised Image Rectification via Seeing the Same Scene from
Multiple Different Lenses [82.56853587380168]
We propose a novel self-supervised image rectification (SIR) method based on an important insight that the rectified results of distorted images of the same scene from different lens should be the same.
We leverage a differentiable warping module to generate the rectified images and re-distorted images from the distortion parameters.
Our method achieves comparable or even better performance than the supervised baseline method and representative state-of-the-art methods.
arXiv Detail & Related papers (2020-11-30T08:23:25Z) - An approach to human iris recognition using quantitative analysis of
image features and machine learning [0.5243460995467893]
In this paper, an efficient framework for iris recognition is proposed in four steps.
The results confirm that the proposed scheme can provide a reliable prediction with an accuracy of up to 99.64%.
arXiv Detail & Related papers (2020-09-12T23:23:33Z) - Category Level Object Pose Estimation via Neural Analysis-by-Synthesis [64.14028598360741]
In this paper we combine a gradient-based fitting procedure with a parametric neural image synthesis module.
The image synthesis network is designed to efficiently span the pose configuration space.
We experimentally show that the method can recover orientation of objects with high accuracy from 2D images alone.
arXiv Detail & Related papers (2020-08-18T20:30:47Z) - Fine-grained Image-to-Image Transformation towards Visual Recognition [102.51124181873101]
We aim at transforming an image with a fine-grained category to synthesize new images that preserve the identity of the input image.
We adopt a model based on generative adversarial networks to disentangle the identity related and unrelated factors of an image.
Experiments on the CompCars and Multi-PIE datasets demonstrate that our model preserves the identity of the generated images much better than the state-of-the-art image-to-image transformation models.
arXiv Detail & Related papers (2020-01-12T05:26:47Z)
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.