Accurate Iris Localization Using Edge Map Generation and Adaptive Circular Hough Transform for Less Constrained Iris Images

This paper proposes an accurate iris localization algorithm for the iris images acquired under near infrared (NIR) illuminations and having noise due to eyelids, eyelashes, lighting reflections, non-uniform illumination, eyeglasses and eyebrow hair etc. The two main contributions in the paper are an edge map generation technique for pupil boundary detection and an adaptive circular Hough transform (CHT) algorithm for limbic boundary detection, which not only make the iris localization more accurate but faster also. The edge map for pupil boundary detection is generated on intersection (logical AND) of two binary edge maps obtained using thresholding, morphological operations and Sobel edge detection, which results in minimal false edges caused by the noise. The adaptive CHT algorithm for limbic boundary detection searches for a set of two arcs in an image instead of a full circle that counters iris-occlusions by the eyelids and eyelashes. The proposed CHT and adaptive CHT implementations for pupil and limbic boundary detection respectively use a two-dimensional accumulator array that reduces memory requirements. The proposed algorithm gives the accuracies of 99.7% and 99.38% for the challenging CASIA-Iris-Thousand (version 4.0) and CASIA-Iris-Lamp (version 3.0) databases respectively. The average time cost per image is 905 msec. The proposed algorithm is compared with the previous work and shows better results

Occluded iris classification and segmentation using self-customized artificial intelligence models and iterative randomized Hough transform

A fast and accurate iris recognition system is presented for noisy iris images, mainly the noises due to eye occlusion and from specular reflection. The proposed recognition system will adopt a self-customized support vector machine (SVM) and convolution neural network (CNN) classification models, where the models are built according to the iris texture GLCM and automated deep features datasets that are extracted exclusively from each subject individually. The image processing techniques used were optimized, whether the processing of iris region segmentation using iterative randomized Hough transform (IRHT), or the processing of the classification, where few significant features are considered, based on singular value decomposition (SVD) analysis, for testing the moving window matrix class if it is iris or non-iris. The iris segments matching techniques are optimized by extracting, first, the largest parallel-axis rectangle inscribed in the classified occluded-iris binary image, where its corresponding iris region is crosscorrelated with the same subject’s iris reference image for obtaining the most correlated iris segments in the two eye images. Finally, calculating the iriscode Hamming distance of the two most correlated segments to identify the subject’s unique iris pattern with high accuracy, security, and reliability

Evaluating the impact of image preprocessing on iris segmentation

La segmentación del iris es una de las etapas más importantes en los sistemas de reconocimiento del iris. En este trabajo se aplican algoritmos de preprocesamiento de la imagen con el objetivo de evaluar su impacto en los porcentajes de segmentación exitosa del iris. Los algoritmos utilizados se basan en el ajuste del histograma, filtros Gaussianos y en la eliminación del reflejo especular en imágenes del ojo humano. Se aplica el método de segmentación introducido por Masek a 199 imágenes tomadas bajo condiciones no controladas, pertenecientes a la base de datos CASIA-irisV3, antes y después de aplicar los algoritmos de preprocesamiento. Posteriormente se evalúa el impacto de los algoritmos de preprocesamiento en el porcentaje de segmentación exitosa del iris por medio de una inspección visual de las imágenes, para determinar si las circunferencias detectadas del iris y de la pupila corresponden adecuadamente con el iris y la pupila de la imagen real. El algoritmo que generó uno de los mayores incrementos de los porcentajes de segmentación exitosa (pasa de 59% a 73%) es aquel que combina la eliminación de reflejos especulares, seguido por la aplicación de un filtro Gaussiano con máscara 5x5. Los resultados obtenidos señalan la importancia de una etapa previa de preprocesamiento de la imagen como paso previo para garantizar una mayor efectividad en el proceso de detección de bordes y segmentación del iris.Segmentation is one of the most important stages in iris recognition systems. In this paper, image preprocessing algorithms are applied in order to evaluate their impact on successful iris segmentation. The preprocessing algorithms are based on histogram adjustment, Gaussian filters and suppression of specular reflections in human eye images. The segmentation method introduced by Masek is applied on 199 images acquired under unconstrained conditions, belonging to the CASIA-irisV3 database, before and after applying the preprocessing algorithms. Then, the impact of image preprocessing algorithms on the percentage of successful iris segmentation is evaluated by means of a visual inspection of images in order to determine if circumferences of iris and pupil were detected correctly. An increase from 59% to 73% in percentage of successful iris segmentation is obtained with an algorithm that combine elimination of specular reflections, followed by the implementation of a Gaussian filter having a 5x5 kernel. The results highlight the importance of a preprocessing stage as a previous step in order to improve the performance during the edge detection and iris segmentation processes

A multi-biometric iris recognition system based on a deep learning approach

YesMultimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris- V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person

KLASIFIKASI DAN PENGOLAHAN CITRA IRIS PASIEN GAGAL GINJAL KRONIS (CHRONIC RENAL FAILURE) DENGAN MENGGUNAKAN ALGORITMA WATERSHED DAN SUPPORT VECTOR MACHINE (SVM)

Iridology adalah metode alternatif yang dapat digunakan untuk mendeteksi kerusakan organ. Dengan mengamati kerusakan jaringan dalam iris pada area tertentu dapat merepresentasikan adanya perubahan fungsi organ tubuh tertentu. Tingkat kerusakan suatu organ dapat ditunjukkan dengan melihat pola kerusakan jaringan di iris. Ginjal merupakan salah satu organ tubuh yang dapat dilihat kondisinya dengan melihat keadaaan iris. Fokus penelitian ini terbatas pada analisis iris pasien gagal ginjal kronis yang sudah melakukan terapi Hemodialisis. Jumlah pasien yang ikut serta dalam penelitian ini sebanyak 61 orang. Peneliti juga mengambil citra iris orang normal dan mendekati normal sebanyak 21 orang. Pengambilan citra iris setiap peserta dilakukan dengan menggunakan kamera iridology. Algoritma watershed digunakan untuk ekstraksi fitur dari citra iris. Daerah yang secara spesifik diteliti pada lingkaran iris berada pada posisi 5.35 - 5.95 (2520 – 2680) untuk mata kanan dan 6.05 - 6.60 (2720 - 2880) untuk mata kiri dengan asumsi seluruh lingkaran iris dibagi 120 (3600) yang merepresentasikan ginjal kanan dan kiri. Dari hasil penelitian diperoleh bahwa iridology dapat digunakan sebagai metode alternatif lain yang dapat digunakan untuk mendeteksi kesehatan ginjal. Hal ini terlihat bahwa dari keseluruhan pasien gagal ginjal yang diambil datanya 87.5% menunjukkan tanda kerusakan jaringan di iris mata kanan dan 89.3% menunjukkan tanda kerusakan jaringan di iris mata kiri. Dari keseluruhan partisipan orang normal dan mendekati normal hanya 38% yang tidak menunjukkan tanda kerusakan jaringan di iris mata kanan dan 61.9% tidak menunjukkan adanya tanda kerusakan jaringan di iris mata kiri. Dari keseluruhan percobaan dengan menggunakan SVM diperoleh rata – rata akurasi terbaik 87.5% dan rata- rata recall terbaik 91.7% yang dihasilkan oleh percentage split 90. Pada penelitian ini dataset yang digunakan untuk data latih dan data uji adalah sama. ======================================================================= Iridology is one alternative ways to know the condition of the human organs. In iridology, the existance of broken tissue on the iris image in a certain area is representing the condition of a specific organ. Renal or kidneys are the example of the organs that can be seen through the iris. Focus of this research is to analyze the iris image of patient Chronic Renal Failure (CRF). According to the GFR (Glomerular Fitration Rate) in the blood of the patients, CRF could reach 5 stages. In this book the analysis was limited to the patients of CRF who have already been in hemodialysis treatment (stage 5). The number of hemodialysis patients who participated in this research was 61 people and 21 people with normal or nearest normal kidney. Iris image of CRF patients were taken using specific iris camera. Watershed transform technique was used to extract the features of iris image of hemodialysis patients. The ROI (region of interest) of iris image of renal organ is at 5.35-5.95 (2520 – 2680) for right eye and at 6.05-6.6 (2720 - 2880) for left eye assuming that the circle of iris is divided into 120 points (3600). The medical records of participants were used to validate the result of this iridology study.The result shows that 87.5% of patients hemodialysis has shown broken tissue on their right iris and 89.3% has shown broken tissue on their left iris. There are 38% of the normal and nearest normal participants shown that there are no sign of broken tissues in their right eye and 68.1% for the left eye. In conclusion, the condition of renal organ of CRF patients can be seen through the broken tissue in iris image. From all the experiments with SVM model for learning and testing dataset, best mean of precission is 87.5% and best mean recall is 91.7% given by percentage split 90 (where the data training was 90% and data testing was 10%). In this research the dataset for training and testing was same

A Hybrid Multibiometric System for Personal Identification Based on Face and Iris Traits. The Development of an automated computer system for the identification of humans by integrating facial and iris features using Localization, Feature Extraction, Handcrafted and Deep learning Techniques.

Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. This PhD thesis is focused on the combination of both the face and the left and right irises, in a unified hybrid multimodal biometric identification system using different fusion approaches at the score and rank level. Firstly, the facial features are extracted using a novel multimodal local feature extraction approach, termed as the Curvelet-Fractal approach, which based on merging the advantages of the Curvelet transform with Fractal dimension. Secondly, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the deep learning approaches is proposed, Multimodal Deep Face Recognition (MDFR) framework, to address the face recognition problem in unconstrained conditions. Thirdly, an efficient deep learning system is employed, termed as IrisConvNet, whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from an iris image. Finally, The performance of the unimodal and multimodal systems has been evaluated by conducting a number of extensive experiments on large-scale unimodal databases: FERET, CAS-PEAL-R1, LFW, CASIA-Iris-V1, CASIA-Iris-V3 Interval, MMU1 and IITD and MMU1, and SDUMLA-HMT multimodal dataset. The results obtained have demonstrated the superiority of the proposed systems compared to the previous works by achieving new state-of-the-art recognition rates on all the employed datasets with less time required to recognize the person’s identity.Higher Committee for Education Development in Ira