Iris spoofing detection using discrete orthogonal moments

Kaur, Bineet

doi:10.1007/s11042-019-08281-x

Cited by 8 publications

(3 citation statements)

References 42 publications

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“…The retinal blood vessels could be distinguished from the surrounding tissue using an important shape descriptor. The RCM is a discrete orthogonal moment that offers inherent advantages for feature extraction among the many moment families 16 , 30 . Its discrete qualities offer certain advantages owing to the computational complexity caused by normalization as well as the computational error produced by approximations and its orthogonal basis leads to less information redundancy.…”

Section: Proposed Retinal Identification Systemmentioning

confidence: 99%

“…14 The structural moment invariant improved the image object shape recognition (RCM) by replacing the original density functions with new ones that were invariant in terms of translation, scale, and orientation and can characterize the form of complicated 2D objects. Kaur et al 15,16 proposed a method for iris recognition in uncontrolled environments employing discrete orthogonal moment-based characteristics, such as Tchebichef, Krawtchouk, and Dual-Ahn. A proposed feature set comprised of Zernike moments and polar harmonic transforms is invariant to rotation, noise, and illumination.…”

Section: Related Workmentioning

confidence: 99%

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Human retinal biometric recognition system based on multiple feature extraction

Marappan

Murugesan²,

Malar

et al. 2023

J. Electron. Imag.

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Retinal biometric trait is one of the safest and most secure biometric traits that can be used for personal identification and authentication. The poor contrast in captured retinal images initially requires the adoption of a preprocessing technique, especially when the vessel segmentation is required. As a result, an effective preprocessing technique is employed before the feature extraction process. The reliability of the segmentation depends on the consistency of the contrast across the images. Due to the complexity of the retinal vascular patterns, segmenting the vessels manually is a time-consuming task that also requires extensive training and abilities to distinguish the thinner blood vessel pixels from the background of the retinal images. We present a unique blood vessel segmentation technique based on Radial Chebyshev Moments (RCMs) shape descriptors. Moment-based shape descriptors are typically strong, concise, and simple to compute. They are also insensitive to the object's scale, rotation, and translation. Furthermore, to eliminate the eye movement and to overcome the various lighting problems, the following multiple features are extracted in the feature extraction stage to accurately authenticate a person: statistical (gray-level) features, shape-based (RCM) features, and structural features, such as minutiae, bifurcation points, optical disc, and enclosed regions. The principal components analysis methodology is used to optimize the feature vector, and the target image is identified using the support vector machine classifier. This step helps to determine whether the test image belongs to the authorized person or not. RIDB and VARIA are the two standard retinal authentication databases and DRIVE and STARE are the two general retinal databases that are used for evaluating the performance of the system. The system yielded a high identification rate of 98%, 97.5%, 97.2% and 94.4% with average processing time of 0.987 s, 0.40 s, 1.262 s and 0.65 s, respectively. From the results, it is evident that the proposed method is more effective and efficient for authenticating individuals.

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Section: Proposed Retinal Identification Systemmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Human retinal biometric recognition system based on multiple feature extraction

Marappan

Murugesan²,

Malar

et al. 2023

J. Electron. Imag.

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“…Iris recognition can be utilized from a distance, help maintain personal hygiene, and minimize virus infection . Moreover, iris recognition offers many benefits, including the fact that it is difficult to forge (Cohen et al, 2021), spoof-proof (Kaur, 2020), naturally protected from harsh environments (Sujatha & Chilambuchelvan, 2018), gives accurate matching (Wang & Kumar, 2019), and offers high scalability (Shin et al, 2017). This system contains a massive iris database that can be obtained and accessed through numerous government entities (Chen et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Initial Contour and Partly-Normalization Algorithm for Iris Segmentation of Blurry Iris Images

Jamaludin

Ayob

Norzeli

et al. 2022

Journal of Information and Communication Technology

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Iris segmentation is a process to isolate the accurate iris region from the eye image for iris recognition. Iris segmentation on non-ideal and noisy iris images is accurate with active contour. Nevertheless, it is currently unclear on how active contour responds to blurry iris images or motion blur, which presents a significant obstacle in iris segmentation. Investigation on blurry iris images, especially on the initial contour position, is rarely published and must be clarified. Moreover, evolution or convergence speed remains a significant challenge for active contour as it segments the precise iris boundary. Therefore, this study carried out experiments to achieve an efficient iris segmentation algorithm in terms of accuracy and fast execution, according to the aforementioned concerns. In addition, initial contour was explored to clarify its position. In order to accomplish these goals, the Wiener filter and morphological closing were used for preprocessing and reflection removal. Next, the adaptive initial contour (AIC), δ, and stopping function were integrated to create the adaptive Chan-Vese active contour (ACVAC) algorithm. Finally, the partly -normalization method for normalization and feature extraction was designed by selecting the most prominent iris features. The findings revealed that the algorithm outperformed the other active contour-based approaches in computational time and segmentation accuracy. It proved that in blurry iris images, the accurate initial contour position could be established. This algorithm is significant to solve inaccurate segmentation on blurry iris images.

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On Data-Driven Approaches for Presentation Attack Detection in Iris Recognition Systems

Sharma

Selwal

2021

Lecture Notes in Electrical Engineering

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Iris spoofing detection using discrete orthogonal moments

Cited by 8 publications

References 42 publications

Human retinal biometric recognition system based on multiple feature extraction

Human retinal biometric recognition system based on multiple feature extraction

Adaptive Initial Contour and Partly-Normalization Algorithm for Iris Segmentation of Blurry Iris Images

On Data-Driven Approaches for Presentation Attack Detection in Iris Recognition Systems

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