Iris presentation attack detection based on best-k feature selection from YOLO inspired RoI

Choudhary, Meenakshi; Tiwari, Vivek; Uduthalapally, Venkanna

doi:10.1007/s00521-020-05342-3

Cited by 17 publications

(6 citation statements)

References 39 publications

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“…e authors of [14,15] improved a deep learning network to locate human eyes in binocular images. In the study by Choudhary et al [16], in order to distinguish living from forged irises, the region of interest (ROI) of the image was obtained using the YOLO [17] network, and a convolutional neural network [18] was used to extract the distinctive texture features. In practical applications, the effective iris area of an eye image usually occupies a small area of the entire image, while other areas are noniris area and can be disregarded, which reduce the localization accuracy of algorithms due to the presence of uneven illumination, hair, eyelids, glasses, and other interference, as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Iris Localization Algorithm based on Effective Area

Zhang

Wang

2021

International Journal of Antennas and Propagation

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Iris localization is the most crucial part of the iris processing because its accuracy can directly affect the accuracy of biometric identification in subsequent steps. Yet, the quality of iris images may be sharply degraded due to interference from eyelashes and reflections during image acquisition, which can affect the localization accuracy adversely. To solve the problem, an iris localization algorithm based on effective area is proposed. First, YOLOv4 is used to crop the image to obtain the effective iris area, which is beneficial in improving the accuracy of subsequent localization. Furthermore, a method to remove reflective noise is proposed, which can effectively avoid the problem of noise interference in the process of inner boundary determination. Finally, aiming at the edge deviation caused by eyelashes, an outer boundary adjustment method is proposed. The experimental results show that the proposed method achieves good performance in the localization of iris images of both good quality and noise interference and outperforms other state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Iris Localization Algorithm based on Effective Area

Zhang

Wang

2021

International Journal of Antennas and Propagation

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Section: Presentation Attack Detection For Mutile-modelmentioning

confidence: 99%

“…In contrast, to feature fusion, [147][148][149] use other methods to improve the PAD model's feature extraction ability or classification ability. [148] uses the YOLO to localize the RoI in pre-processing. Iris texture features in the RoI are then extracted using a variety of manual and CNN-based methods.…”

Section: Presentation Attack Detection For Mutile-modelmentioning

confidence: 99%

Deep Learning for Iris Recognition: A Review

Yin¹,

He²,

Zhang³

et al. 2023

Preprint

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Iris recognition is a secure biometric technology known for its stability and privacy. With no two irises being identical and little change throughout a person's lifetime, iris recognition is considered more reliable and less susceptible to external factors than other biometric recognition methods. Unlike traditional machine learning-based iris recognition methods, deep learning technology does not rely on feature engineering and boasts excellent performance. This paper collects 120 relevant papers to summarize the development of iris recognition based on deep learning. We first introduce the background of iris recognition and the motivation and contribution of this survey. Then, we present the common datasets widely used in iris recognition. After that, we summarize the key tasks involved in the process of iris recognition based on deep learning technology, including identification, segmentation, presentation attack detection, and localization. Finally, we discuss the challenges and potential development of iris recognition. This review provides a comprehensive sight of the research of iris recognition based on deep learning.

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“…The explored six basic augmentations in this study are: horizontal shift, vertical shift, brightness adjustment, zoom in/out, and horizontal flipping. Such augmentation techniques are widely used in the computer vision field with proved positive effect [10,23,32] and also in the iris PAD field [6,7,19,30]. More reasons that lead us to choose these augmentations are: (1) even under a controlled environment, the irises are not in the same position and same viewpoint.…”

Section: Data Augmentation Techniquesmentioning

confidence: 99%

“…Therefore, developing a reliable iris PAD algorithm is still a challenging task. Considering that neural networks successfully improve the performance in many computer vision fields, deep learning-based algorithms are further applied for iris et al [30], Chen et al [6] and Choudhart et al [7] also utilized the augmentation techniques to avoid the overfitting in training phase (see Table 1). However, the contribution of augmentation techniques is not clear because no analysis or experimental comparison is provided as summarized in Table 1.…”

Section: Introductionmentioning

confidence: 99%

The overlapping effect and fusion protocols of data augmentation techniques in iris PAD

Fang

Damer

Kirchbuchner

et al. 2021

Machine Vision and Applications

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Iris Presentation Attack Detection (PAD) algorithms address the vulnerability of iris recognition systems to presentation attacks. With the great success of deep learning methods in various computer vision fields, neural network-based iris PAD algorithms emerged. However, most PAD networks suffer from overfitting due to insufficient iris data variability. Therefore, we explore the impact of various data augmentation techniques on performance and the generalizability of iris PAD. We apply several data augmentation methods to generate variability, such as shift, rotation, and brightness. We provide in-depth analyses of the overlapping effect of these methods on performance. In addition to these widely used augmentation techniques, we also propose an augmentation selection protocol based on the assumption that various augmentation techniques contribute differently to the PAD performance. Moreover, two fusion methods are performed for more comparisons: the strategy-level and the score-level combination. We demonstrate experiments on two fine-tuned models and one trained from the scratch network and perform on the datasets in the Iris-LivDet-2017 competition designed for generalizability evaluation. Our experimental results show that augmentation methods improve iris PAD performance in many cases. Our least overlap-based augmentation selection protocol achieves the lower error rates for two networks. Besides, the shift augmentation strategy also exceeds state-of-the-art (SoTA) algorithms on the Clarkson and IIITD-WVU datasets.

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Iris presentation attack detection based on best-k feature selection from YOLO inspired RoI

Cited by 17 publications

References 39 publications

Iris Localization Algorithm based on Effective Area

Iris Localization Algorithm based on Effective Area

Deep Learning for Iris Recognition: A Review

The overlapping effect and fusion protocols of data augmentation techniques in iris PAD

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