Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition

Jenadeleh, Mohsen; Pedersen, Marius; Saupe, Dietmar

doi:10.3390/s20051308

Cited by 14 publications

(5 citation statements)

References 66 publications

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“…In the work presented in the paper [9], the authors proposed an idea to calculate the quality of iris image. At the beginning, it was claimed that poor quality samples can have a huge influence on false rejection rate (FRR) increasement as well as decrease in the system performance (in terms of accuracy).…”

Section: How Others See Itmentioning

confidence: 99%

Iris-based human identity recognition with machine learning methods and discrete fast Fourier transform

Szymkowski

Jasiński

Saeed

2021

Innovations Syst Softw Eng

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One of the most important modules of computer systems is the one that is responsible for user safety. It was proven that simple passwords and logins cannot guarantee high efficiency and are easy to obtain by the hackers. The well-known alternative is identity recognition based on biometrics. In recent years, more interest was observed in iris as a biometrics trait. It was caused due to high efficiency and accuracy guaranteed by this measurable feature. The consequences of such interest are observable in the literature. There are multiple, diversified approaches proposed by different authors. However, neither of them uses discrete fast Fourier transform (DFFT) components to describe iris sample. In this work, the authors present their own approach to iris-based human identity recognition with DFFT components selected with principal component analysis algorithm. For classification, three algorithms were used—k-nearest neighbors, support vector machines and artificial neural networks. Performed tests have shown that satisfactory results can be obtained with the proposed method.

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Section: How Others See Itmentioning

confidence: 99%

Iris-based human identity recognition with machine learning methods and discrete fast Fourier transform

Szymkowski

Jasiński

Saeed

2021

Innovations Syst Softw Eng

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“…Image compression techniques cause different types of visual abnormalities in images, such as blocking artifacts, color shift, blurring effects, and ringing artifacts, that result in the degradation of the image quality [ 6 ]. Therefore, while introducing a new image compression technique, quality assessment techniques should be used to evaluate the performance and to consider the severity of the visual abnormalities produced [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Subjective Assessment of Objective Image Quality Metrics Range Guaranteeing Visually Lossless Compression

Afnan

Ullah

Yaseen

et al. 2023

Sensors

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The usage of media such as images and videos has been extensively increased in recent years. It has become impractical to store images and videos acquired by camera sensors in their raw form due to their huge storage size. Generally, image data is compressed with a compression algorithm and then stored or transmitted to another platform. Thus, image compression helps to reduce the storage size and transmission cost of the images and videos. However, image compression might cause visual artifacts, depending on the compression level. In this regard, performance evaluation of the compression algorithms is an essential task needed to reconstruct images with visually or near-visually lossless quality in case of lossy compression. The performance of the compression algorithms is assessed by both subjective and objective image quality assessment (IQA) methodologies. In this paper, subjective and objective IQA methods are integrated to evaluate the range of the image quality metrics (IQMs) values that guarantee the visually or near-visually lossless compression performed by the JPEG 1 standard (ISO/IEC 10918). A novel “Flicker Test Software” is developed for conducting the proposed subjective and objective evaluation study. In the flicker test, the selected test images are subjectively analyzed by subjects at different compression levels. The IQMs are calculated at the previous compression level, when the images were visually lossless for each subject. The results analysis shows that the objective IQMs with more closely packed values having the least standard deviation that guaranteed the visually lossless compression of the images with JPEG 1 are the feature similarity index measure (FSIM), the multiscale structural similarity index measure (MS-SSIM), and the information content weighted SSIM (IW-SSIM), with average values of 0.9997, 0.9970, and 0.9970 respectively.

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“…The reliability of iris is high compared to other biometric which refers to how dependable it is for recognition purpose [9]. While the uniqueness of the iris is also high which means the iris is distinct from each other [10] make it extensively investigated for accurate and personal identification [11].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Iris Recognition Technique using CNN and Vision Transformer

Samihah Abdul Latif,

Khairul Azami Sidek,

Aisha Hassan Abdalla Hashim

2023

ARASET

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The usage of biometric identification has increased in recent years, with numerous public and commercial organizations incorporating biometric technologies into their infrastructures. One of the technologies is iris recognition which has been used as a biometric recognition compared to other modalities to combat identity abuse due to its ability to eliminate risk of collisions or false matches even when comparing large populations. The use of CNN is proven to provide high accuracy; however, this technology involves the need for a large dataset and higher computational cost. Therefore, this study uses a combined model of Convolutional Neural Network (CNN) and Vision Transformer (ViT) in identifying and verifying an iris image. By using the proposed learning rate, it proves that the novel hybrid model is capable to achieve up to 93.66% accuracy in recognizing iris images. The cross-entropy loss function was implemented to reduce the loss and it was able to predict the class label more correctly. In addition, the model was thoroughly tested on three publicly available iris databases, achieving satisfactory iris recognition results. Furthermore, this model has the potential to be used in other biometrics such as face and retina recognitions.

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Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition

Cited by 14 publications

References 66 publications

Iris-based human identity recognition with machine learning methods and discrete fast Fourier transform

Iris-based human identity recognition with machine learning methods and discrete fast Fourier transform

Subjective Assessment of Objective Image Quality Metrics Range Guaranteeing Visually Lossless Compression

An Efficient Iris Recognition Technique using CNN and Vision Transformer

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