Iris Recognition in Cases of Eye Pathology

Trokielewicz, Mateusz; Czajka, Adam; Maciejewicz, Piotr

doi:10.1007/978-981-13-1144-4_2

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…Even pictures of parts of the face allow to derive personal attributes like the gender, age, ethnical background, etc. as well as certain health issues [37]. The work presented in that paper indicates that it is possible to identify illnesses such as glaucoma and cataracts based even on single images.…”

Section: Privacy Concerns In the Evaluation Of Biometric Datamentioning

confidence: 90%

Pros and cons of comparing and combining hand-crafted and neural network based DeepFake detection based on eye blinking behavior

Siegel¹,

Seidlitz²,

Kraetzer³

et al. 2023

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Temporal feature spaces are a promising approach for Deep-Fake detection, since DeepFake synthesis is most often done on a frame-by-frame basis. With the existing and upcoming regulations on European level, the EU General Data Protection Regulation (EU GDPR) and Artificial Intelligence Act (AIA) in particular, data minimization and decision transparency are of concern also for such media forensic methods. In order to bring these aspects together, this paper utilizes two different algorithms both analyzing the eye blinking in the videos. The first one is implemented using deep learning to predict blinking behavior. It shows challenges of hyper-parameter tuning for the training of such a model. The second detector uses an existing hand-crafted approach to identify a suitable number of frames (i.e., video duration) required to reliably detect DeepFakes. Considering GDPR concerns, an optimal trade-off between detection performance and data minimization is found in the range of 35 to 40 seconds of video, giving a detection accuracy of 96.88% for the DeepFakes tested.

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Section: Privacy Concerns In the Evaluation Of Biometric Datamentioning

confidence: 90%

Pros and cons of comparing and combining hand-crafted and neural network based DeepFake detection based on eye blinking behavior

Siegel¹,

Seidlitz²,

Kraetzer³

et al. 2023

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show abstract

“…The paper [19] suggested a code-level plan in heterogeneous iris recognition. A modified Markov network was used to illustrate the non-linear link between binary elements coding of diverse iris pictures.…”

Section: Introductionmentioning

confidence: 99%

Iris recognition method based on segmentation

Mahameed¹,

Ahmed²,

Abdullah³

2022

Eureka: PE

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The development of science and studies has led to the creation of many modern means and technologies that focused and directed their interests on enhancing security due to the increased need for high degrees of security and protection for individuals and societies. Hence identification using a person's vital characteristics is an important privacy topic for governments, businesses and individuals. A lot of biometric features such as fingerprint, facial measurements, acid, palm, gait, fingernails and iris have been studied and used among all the biometrics, in particular, the iris gets the attention because it has unique advantages as the iris pattern is unique and does not change over time, providing the required accuracy and stability in verification systems. This feature is impossible to modify without risk. When identifying with the iris of the eye, the discrimination system only needs to compare the data of the characteristics of the iris of the person to be tested to determine the individual's identity, so the iris is extracted only from the images taken. Determining correct iris segmentation methods is the most important stage in the verification system, including determining the limbic boundaries of the iris and pupil, whether there is an effect of eyelids and shadows, and not exaggerating centralization that reduces the effectiveness of the iris recognition system. There are many techniques for subtracting the iris from the captured image. This paper presents the architecture of biometric systems that use iris to distinguish people and a recent survey of iris segmentation methods used in recent research, discusses methods and algorithms used for this purpose, presents datasets and the accuracy of each method, and compares the performance of each method used in previous studies

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“…However, biometrics deal with human identity recognition (and it has origins in computer science) whilst biometry deals with the statistical methods of biological tissue measurements and it is mostly associated with medicine. Several biometric techniques for human identification and authentication have been proposed [7][8][9][10][11]: fingerprint, hand geometry, palm print, face, iris, retina, ear and eye movements, voice, signature, body odor etc. There are no two identical retinas and even the right and left eye of one person are not the same.…”

Section: Introductionmentioning

confidence: 99%

A Novelty Approach to Retina Diagnosing Using Biometric Techniques With SVM and Clustering Algorithms

et al. 2020

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A method to extract the retina characteristic points for the purpose of medical diagnosis of the human eye is presented in this research. The proposed method helps to make the primary decision about the illness faster and can be used on mobile devices. The algorithm is mostly based on the characteristic points (the so-called minutiae). These structures are commonly used in the biometric applications for fingerprintbased people recognition. In the case of the conducted research, this trait was used to differentiate healthy eyes from unhealthy ones. The methods were evaluated by appropriately implemented algorithms, showing promising results. Each solution was created with object-oriented programming language. The accuracy of the classification (healthy versus samples with pathological changes) was evaluated using four algorithms: k-Nearest Neighbors, k-Means and Support Vector Machines (SVM) with linear and third-degree polynomial as well as our own approach based on counting the minutiae number. Performance requirements were also checked, and it was verified that the computing power of modern mobile devices is sufficient to implement the proposed solution. The highest accuracy result was equal to 96,45% and was obtained with the third-degree polynomial SVM. This was a novel approach. For comparative purposes, we also implemented currently used solutions for image analysis-deep learning (DL) and Convolution Neural Networks (CNNs). Both medical and computer science backgrounds are presented in the work with the main methodology components to include image segmentation using the Gaussian Matched Filter, binarization by Local Entropy Thresholding and classification with the previously mentioned approaches.

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Iris Recognition in Cases of Eye Pathology

Cited by 5 publications

References 31 publications

Pros and cons of comparing and combining hand-crafted and neural network based DeepFake detection based on eye blinking behavior

Pros and cons of comparing and combining hand-crafted and neural network based DeepFake detection based on eye blinking behavior

Iris recognition method based on segmentation

A Novelty Approach to Retina Diagnosing Using Biometric Techniques With SVM and Clustering Algorithms

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