Investigation of iris segmentation techniques using active contours for non-cooperative iris recognition

Akinfende, Akinola Samuel; Imoize, Agbotiname Lucky; Ajose, Olumide Simeon

doi:10.11591/ijeecs.v19.i3.pp1275-1286

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…The actual acceptance rate (GAR) for the face was 87.33%, 71.55% for fused irises, and 94.44% for all three fused irises. For more detiales see [13], [14].…”

Section: Iris and The Face Mergementioning

confidence: 99%

Biometric face recognition method using graphics processing unit system

Alshiha

Al-Neama

Al-Qubaa

2023

IJEECS

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The expansion of biometric applications and databases is worrying. Processing extensive or sophisticated biometric data results in longer wait times, which might restrict application usefulness. This work focuses on accelerating the processing of biometric data and proposes a parallel method of data processing that exceeds the capabilities of a central processing unit (CPU). The combination of the graphics processing unit (GPU) and compute unified device architecture (CUDA) results in at least three times the processing speed of a published accurate and secure multimodal biometric system. The GPU-assisted approach beats the CPU-only implementation when saturating the CPU-only performance with more people than the available thread count. The GPU-assisted solution is also proven to have the same accuracy as the original system, indicating accuracy and processing performance improvements in the demanding big data environment.

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“…The actual acceptance rate (GAR) for the face was 87.33%, 71.55% for fused irises, and 94.44% for all three fused irises. For more detiales see [13], [14].…”

Section: Iris and The Face Mergementioning

confidence: 99%

Biometric face recognition method using graphics processing unit system

Alshiha

Al-Neama

Al-Qubaa

2023

IJEECS

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“…Accuracy is 93.33% [30] Circular Hough transform with active contour CAISA 88.3% of accuracy www.ijacsa.thesai.org activity of getting an image from a source is known as image acquisition; image segmentation is the process of separating an image into sections or regions; and feature extraction techniques are used to obtain features that will be useful in image classification and recognition. Edge detection will be done by using Canny, Sobel and Prewitt edge detection methods for iris recognition.…”

Section: Casia-vimentioning

confidence: 99%

Iris Recognition Through Edge Detection Methods: Application in Flight Simulator User Identification

Khan¹,

Khan²,

Nwobodo³

et al. 2023

IJACSA

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To meet the increasing security requirement of authorized users of flight simulators, personal identification is becoming more and more important. Iris recognition stands out as one of the most accurate biometric methods in use today. Iris recognition is done through different edge detection methods. Therefore, it is important to have an understanding of the different edge detection methods that are in use these days. Specifically, the biomedical research shows that irises are as different as fingerprints or the other patterns of the recognition. Furthermore, because the iris is a visible organism, its exterior look can be examined remotely using a machine vision system. The main part of this paper delves into concerns concerning the selection of the best results giving method of the recognition. In this paper, three edge detection methods, namely Canny, Sobel and Prewitt, are applied to the image of eye (iris) and their comparative analysis is discussed. These methods are applied using the Software MATLAB. The datasets used for this purpose are CASIA and MMU. The results indicate that the performance of Canny edge detection method is best as compared to Sobel and Prewitt. Image quality is a key requirement in image-based object recognition. This paper provides the quality evaluation of the images using different metrics like PSNR, SNR, MSE and SSIM. However, SSIM is considered best image quality metric as compared to PSNR, SNR and MSE.

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“…Meanwhile, the nonideal iris images with non-uniform illumination and non-circular iris boundary can also be segmented (Jan, 2017). Various works achieved good performance when using the active contour approaches such as those reported in Ouabida et al (2017), Chen et al (2015), Jamaludin et al (2018), Akinfende et al (2020), andJamaludin et al (2017).…”

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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Investigation of iris segmentation techniques using active contours for non-cooperative iris recognition

Cited by 8 publications

References 26 publications

Biometric face recognition method using graphics processing unit system

Biometric face recognition method using graphics processing unit system

Iris Recognition Through Edge Detection Methods: Application in Flight Simulator User Identification

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

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