GHCLNet: A generalized hierarchically tuned contact lens detection network

Singh, Avantika; Mistry, Vishesh; Yadav, D.P.; Nigam, Aditya

doi:10.1109/isba.2018.8311471

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…Fully convolutional encoder-decoder networks to obtain reliable segmentation performance in iris images are considered in [33]. In another direction, various methods have been suggested to address the issue of contact lens detection that involve deep feature computation [34][35][36][37][38][39][40].…”

Section: Resultsmentioning

confidence: 99%

RANSAC lens boundary feature based kernel SVM for transparent contact lens detection

Kumar¹,

Puhan²

2018

IET biom.

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Transparent contact lens spoofing has been demonstrated to hamper the overall performance of an iris recognition system. Achieving high detection accuracy with transparent lens is quite challenging using iris texture analysis based techniques. In this regard, the authors propose a supervised learning based transparent lens detection method by designing a novel set of features to describe the faint lens boundary. The input image is first segmented for extracting salient edge points in the sclera region of interest. RANSAC algorithm then performs circle fitting to generate the set of R-points for feature computation. The novel features such as concentricity parameter, R-point count, R-point intensity, R-circle radius, R-cluster count and circle genuineness are associated with distinctive range of values for each class. Experimental results obtained using the kernel-support vector machine (SVM) classifier show that the proposed method can achieve higher average detection accuracy as compared to state-of-the-art techniques: 90.63% (NotreDame I), 84.5% (NotreDame II), 83% (IIIT-D Cogent) and 86.27% (IIIT-D Vista).

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Section: Resultsmentioning

confidence: 99%

RANSAC lens boundary feature based kernel SVM for transparent contact lens detection

Kumar¹,

Puhan²

2018

IET biom.

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“…The significant limitation is the training complexity because of training the CNN individually over each patch. Choudhary et al [10], Singh et al [47], and Yadav et al [54] have used the ResNet and DenseNet models for contact lens detection. McGrath et al [36] have developed an open-source iris presentation attack detection module utilizing publicly available machine learning feature extraction and classification algorithms.…”

Section: A Related Workmentioning

confidence: 99%

Generalized Iris Presentation Attack Detection Algorithm under Cross-Database Settings

Gupta¹,

Singh²,

Vatsa³

et al. 2020

Preprint

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Presentation attacks are posing major challenges to most of the biometric modalities. Iris recognition, which is considered as one of the most accurate biometric modality for person identification, has also been shown to be vulnerable to advanced presentation attacks such as 3D contact lenses and textured lens. While in the literature, several presentation attack detection (PAD) algorithms are presented; a significant limitation is the generalizability against an unseen database, unseen sensor, and different imaging environment. To address this challenge, we propose a generalized deep learning-based PAD network, MVANet, which utilizes multiple representation layers. It is inspired by the simplicity and success of hybrid algorithm or fusion of multiple detection networks. The computational complexity is an essential factor in training deep neural networks; therefore, to reduce the computational complexity while learning multiple feature representation layers, a fixed base model has been used. The performance of the proposed network is demonstrated on multiple databases such as IIITD-WVU MUIPA and IIITD-CLI databases under cross-database training-testing settings, to assess the generalizability of the proposed algorithm.

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“… DensePAD [13]: DensePAD is designed based on DenseNet with a depth of 22 to classify input iris images as real or iris spoofing with textured contact lenses.  GHCLNet [14]: GHCLNet is a CNN based on ResNet-50 and can be classified into three categories: no, textured, and soft lens, without preprocessing of the iris image.…”

mentioning

confidence: 99%

“…Among these previous studies, [11][12][13][14][15][16][17][18][19] focused on determining whether contact lenses were worn based on iris images, whereas [20] focused on determining whether colored contact lenses were defective. However, most contact-lens-related studies using CNNs are related to contact lens discrimination for iris recognition systems, and few studies on CNN-based lens defect detection have been conducted.…”

mentioning

confidence: 99%

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A Deep Learning Technique for Optical Inspection of Color Contact Lenses

Kim

Park²,

Moon³

et al. 2023

Applied Sciences

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Colored contact lenses have gained popularity in recent years. However, their production process is plagued by low efficiency, which is attributed to the complex nature of the lens color patterns. The manufacturing process involves multiple complex steps that can introduce defects or inconsistencies into the contact lenses. Moreover, manual inspection of a considerable number of contact lenses that are produced inefficiently in terms of consistency and quality by humans is prevalent. Alternatively, automatic optical inspection (AOI) systems have been developed to perform quality-control checks on colored contact lenses. However, their accuracy is limited due to the increasing complexity of the lens color patterns. To address these issues, convolutional neural networks have been used to detect and classify defects in colored contact lenses. This study aims to provide a comprehensive guide for AOI systems using artificial intelligence in the colored contact lens manufacturing process, including the benefits and challenges of using these systems. Further, future research directions to achieve a classification accuracy of >95%, which is the human recognition rate, are explored.

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GHCLNet: A generalized hierarchically tuned contact lens detection network

Cited by 13 publications

References 12 publications

RANSAC lens boundary feature based kernel SVM for transparent contact lens detection

RANSAC lens boundary feature based kernel SVM for transparent contact lens detection

Generalized Iris Presentation Attack Detection Algorithm under Cross-Database Settings

A Deep Learning Technique for Optical Inspection of Color Contact Lenses

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