A comprehensive review of machine learning techniques on diabetes detection

Diabetes mellitus has been an increasing concern owing to its high morbidity, and the average age of individual affected by of individual affected by this disease has now decreased to mid-twenties. Given the high prevalence, it is necessary to address with this problem effectively. Many researchers and doctors have now developed detection techniques based on artificial intelligence to better approach problems that are missed due to human errors. Data mining techniques with algorithms such as - density-based spatial clustering of applications with noise and ordering points to identify the cluster structure, the use of machine vision systems to learn data on facial images, gain better features for model training, and diagnosis via presentation of iridocyclitis for detection of the disease through iris patterns have been deployed by various practitioners. Machine learning classifiers such as support vector machines, logistic regression, and decision trees, have been comparative discussed various authors. Deep learning models such as artificial neural networks and recurrent neural networks have been considered, with primary focus on long short-term memory and convolutional neural network architectures in comparison with other machine learning models. Various parameters such as the root-mean-square error, mean absolute errors, area under curves, and graphs with varying criteria are commonly used. In this study, challenges pertaining to data inadequacy and model deployment are discussed. The future scope of such methods has also been discussed, and new methods are expected to enhance the performance of existing models, allowing them to attain greater insight into the conditions on which the prevalence of the disease depends.

An OCaNet Model Based on Octave Convolution and Attention Mechanism for Iris Recognition

Iris recognition refers to identifying individuals based on iris patterns, which have been widely used in security systems, such as subway security and access control attendance, because everyone has a unique iris shape. In the study, we propose an OCaNet model for the iris recognition task. First, binarized threshold segmentation is used to locate the pupil and the pupil boundary is obtained; then, the Hough transform is applied to locate the outer edge of the iris; according to the located pupil and iris, the iris area image is obtained through image segmentation; finally, the iris image is normalized to adjust each original image to the same size and corresponding position, so as to eliminate the influence of translation, scaling, and rotation on iris recognition. Second, the normalized iris images are both input into the octave convolution module and attention module. The octave convolution module is used to extract the shape and contour features of the iris by decomposing the feature map into high and low frequencies. The attention module is applied to extract the color and texture characteristics of the iris. Finally, the two feature maps are concatenated and produce a distribution of output classes. Experimental results show that the proposed OCaNet model is significantly more accurate.

APLIKASI PENGENALAN IRIS MATA MENGGUNAKAN METODE HOUGH TRANSFORM DAN GABOR WAVELET

Biometric system is a development of the basic method of identification system by using the characteristics of humans as it’s object. These include face, fingerprints, signature, palms, iris, ears, sounds even DNA. Face recognition is one of the identification techniques in biometrics that uses part of the face as its parameter. One of the biometric parts of face is Iris. Iris is a unique part of the eyes, this is because the pattern of the somebody eyes will be quite different from the other, even genetically identical twins have different iris patterns. This research will use the Hough and Gabor method to perform iris recognition. The results show that the application has succeeded in recognizing the selected eye image if the eye image is registered in the database.

Collision Avoidance on National and Global Scales: Understanding and Using Big Biometric Entropy

Large-scale testing involving more than a trillion comparisons between the iris patterns of different persons has confirmed that the entropy of IrisCodes is large enough to deliver collision avoidance (absence of False Matches) in population sizes of national, continental, and even planetary scale. This short paper explains how the entropy of this biometric achieves it. <div><br></div>

Collision Avoidance on National and Global Scales: Understanding and Using Big Biometric Entropy

Large-scale testing involving more than a trillion comparisons between the iris patterns of different persons has confirmed that the entropy of IrisCodes is large enough to deliver collision avoidance (absence of False Matches) in population sizes of national, continental, and even planetary scale. This short paper explains how the entropy of this biometric achieves it. <div><br></div>

A Review of the Iris Recognition Methods Used for the Individual Authentication

The automatic iris recognition has become one of the most important techniques for authenticating the identity of individuals. The analysis of human iris is a reliable tool for the individual authentication due to the iris structure. Iris patterns constitute one of the uniqueness, permanence, and performance biometric traits. Moreover, the iris is considered as not easily tampered biometric traits. Therefore, this paper considers investigating the common automated methods of iris recognition. It surveys the development of utilizing iris images as an authentication means through the explanation of the historical improvement of the processes of the iris analysis. The contribution of this paper is to provide readers with huge information collected and discussed from more than 40 papers of iris recognition studies which have been published in a period of more than 20 years.

Segmentation Approach for a Noisy Iris Images Based on Hybrid Techniques

Iris recognition indicates the procedure of recognizing humans based on their both left and right iris patterns. Nowadays there is rapid progress in realizing an old dream of developing a user-friendly recognition system. Most of the new projects became a nightmare of security of the system. The prosperity of iris recognition aside from its attractive physical characteristics is led to developing an efficient feature extractor to attain the required objective of recognition. Fingerprint, facial, and iris biometric techniques are developed widely for identifying processing most boarded management points, access control, and military checkpoints. Hybridization between Daugman’s Integro Differential Operator (IDO) with edge base methods was realized through taking the advantages of the good qualities of both methods so as to enhance the precision and reduce the required time. The proposed hybrid recognition system is very reliable and accurate. UBIRIS version 1 dataset was utilized in the conducted simulation which indicates the distinctions of the hybrid method in providing good performance and accuracy with reducing the time consuming of iris localization by approximately 99% compared with IDO and edge based methods.

A Robust Method of Eye Torsion Measurement for Medical Applications

The detection of eye torsion is an important element for diagnosis of balance disorders, although it is rarely available in existing eye tracking systems. A novel method is proposed in this paper to provide robust measurement of torsional eye movements. A numerical approach is presented to estimate the iris boundary only according to the gaze direction, so the segmentation of the iris is more robust against occlusions and ambiguities. The perspective distortion of the iris pattern at eccentric eye positions is also corrected, benefiting from the transformation relation that is established for the iris estimation. The angle of the eye torsion is next measured on the unrolled iris patterns via a TM (Template Matching) technique. The principle of the proposed method is validated and its robustness in practice is assessed. A very low mean FPR (False Positive Rate) is reported (i.e., 3.3%) in a gaze test when testing on five participants with very different eye morphologies. The present method always gave correct measurement on the iris patterns with simulated eye torsions and rarely provided mistaken detections in the absence of eye torsion in practical conditions. Therefore, it shows a good potential to be further applied in medical applications.

Iris recognition under the influence of diabetes

In this study, iris recognition under the influence of diabetes was investigated. A new database containing 1318 pictures from 343 irides – 546 images from 162 healthy irides (62% female users, 38% male users, 21% <20 years old, 61% (20) < 40 years old, 12% (40) <60 years old and 6% more than 60 years old) and 772 iris images from 181 diabetic eyes but with a clearly visible iris pattern (80% female users, 20% male users, 1% <20 years old, 17.5% (20) <40 years old, 46.5% (40) <60 years old and 35% more than 60 years old) – were collected. All of the diabetes-affected eyes had clearly visible iris patterns without any visible impairments and only type II diabetic patients with at least 2 years of being diabetic were considered for the investigation. Three different open source iris recognition codes and one commercial software development kit were used for achieving the iris recognition system’s performance evaluation results under the influence of diabetes. For statistical analysis, the t-test and the Kolmogorov-Simonov test were used.