Identifikasi Lokasi Iris Mata Berbasis Tranformasi Hough dan Deteksi Tepi Canny

Sistem identifikasi berbasis keunikan anggota tubuh manusia berkembang pesat di berbagai bidang aplikasi komersial. Iris mata merupakan salah satu dari sistem identifikasi yang dikembangkan. Hal ini mempengaruhi penelitian-penelitian yang mengarah pada kemampuan untuk menjamin tingkat akurasi dan kehandalan  dalam berbagai kesulitan pada lingkungan yang mengandung noise seperti  pemakaian kacamata, rambut, bulu mata, pengaruh blurring. Salah satu tahap yang paling kritis dan mendasar dalam sistem pengenalan iris mata  adalah  mengidentifikasi lokasi iris mata di dalam citra input. meningkatkan akurasi identifikasi lokasi iris mata dengan berbasis metode tranformasi hough dan deteksi tepi canny serta menghilangkan noise. Deteksi tepi canny memiliki kemampuan mengekstrak tepi dengan kebebasan pemilihan parameter yang digunakan dan hough transform memiliki proses komputasi yang cepat. Langkah yang dilakukan yaitu pengambilan sampel citra iris mata, dilanjutkan dengan pemetaan iris mata berbasis deteksi canny, kemudian mendeteksi lokasi iris mata dengan menentukan batas luar dan dalam iris mata, selanjutnya dilakukan proses menghilangkan noise yang mengganggu proses identifikasi lokasi iris mata. Dalam proses uji coba untuk mengukur tingkat akurasi lokasi iris mata digunakan dataset CASIA-IrisV3 Tujuan dari penelitian ini adalah mengedentifikasi lokasi iris mata yang akurat dalam citra ber-noise berbasis hough tranform dan detekti tepi canny. Selain itu diharapkan memberikan manfaat dalam pengembangan sistem aplikasi biometrik berbasis Iris Mata &nbsp

A Fast and Accurate Iris Localization Technique for Healthcare Security System

yesIn the health care systems, a high security level is required to protect extremely sensitive patient records. The goal is to provide a secure access to the right records at the right time with high patient privacy. As the most accurate biometric system, the iris recognition can play a significant role in healthcare applications for accurate patient identification. In this paper, the corner stone towards building a fast and robust iris recognition system for healthcare applications is addressed, which is known as iris localization. Iris localization is an essential step for efficient iris recognition systems. The presence of extraneous features such as eyelashes, eyelids, pupil and reflection spots make the correct iris localization challenging. In this paper, an efficient and automatic method is presented for the inner and outer iris boundary localization. The inner pupil boundary is detected after eliminating specular reflections using a combination of thresholding and morphological operations. Then, the outer iris boundary is detected using the modified Circular Hough transform. An efficient preprocessing procedure is proposed to enhance the iris boundary by applying 2D Gaussian filter and Histogram equalization processes. In addition, the pupil’s parameters (e.g. radius and center coordinates) are employed to reduce the search time of the Hough transform by discarding the unnecessary edge points within the iris region. Finally, a robust and fast eyelids detection algorithm is developed which employs an anisotropic diffusion filter with Radon transform to fit the upper and lower eyelids boundaries. The performance of the proposed method is tested on two databases: CASIA Version 1.0 and SDUMLA-HMT iris database. The Experimental results demonstrate the efficiency of the proposed method. Moreover, a comparative study with other established methods is also carried out

Iris Identification using Keypoint Descriptors and Geometric Hashing

Iris is one of the most reliable biometric trait due to its stability and randomness. Conventional recognition systems transform the iris to polar coordinates and perform well for co-operative databases. However, the problem aggravates to manifold for recognizing non-cooperative irises. In addition, the transformation of iris to polar domain introduces aliasing effect. In this thesis, the aforementioned issues are addressed by considering Noise Independent Annular Iris for feature extraction. Global feature extraction approaches are rendered as unsuitable for annular iris due to change in scale as they could not achieve invariance to ransformation and illumination. On the contrary, local features are invariant to image scaling, rotation and partially invariant to change in illumination and viewpoint. To extract local features, Harris Corner Points are detected from iris and matched using novel Dual stage approach. Harris corner improves accuracy but fails to achieve scale invariance. Further, Scale Invariant Feature Transform (SIFT) has been applied to annular iris and results are found to be very promising. However, SIFT is computationally expensive for recognition due to higher dimensional descriptor. Thus, a recently evolved keypoint descriptor called Speeded Up Robust Features (SURF) is applied to mark performance improvement in terms of time as well as accuracy. For identification, retrieval time plays a significant role in addition to accuracy. Traditional indexing approaches cannot be applied to biometrics as data are unstructured. In this thesis, two novel approaches has been developed for indexing iris database. In the first approach, Energy Histogram of DCT coefficients is used to form a B-tree. This approach performs well for cooperative databases. In the second approach, indexing is done using Geometric Hashing of SIFT keypoints. The latter indexing approach achieves invariance to similarity transformations, illumination and occlusion and performs with an accuracy of more than 98% for cooperative as well as non-cooperative databases

Recognition using SIFT and its Variants on Improved Segmented Iris

Iris is one of the most reliable biometric traits due to its stability and randomness. Iris is transformed to polar coordinates by the conventional recognition systems. They perform well for the cooperative databases, but the performance deteriorates for the non-cooperative irises. In addition to this, aliasing effect is introduced as a result of transforming iris to polar domain. In this thesis, these issues are addressed by considering annular iris free from noise due to eyelids. This thesis presents several SIFT based methods for extracting distinctive invariant features from iris that can be used to perform reliable matching between different views of an object or scene. After localization of the iris, Scale Invariant Feature Transform (SIFT) is used to extract the local features. The SIFT descriptor is a widely used method for matching image features. But SIFT is found out to be computationally very complex. So we use another keypoint descriptor, Speeded up Robust Features (SURF), which is found to be computationally more efficient and produces better results than the SIFT. Both SIFT and SURF has the problem of false pairing. This has been overcome by using Fourier transform with SIFT (called F-SIFT) to obtain the keypoint descriptor and Phase-Only Correlation for feature matching. F-SIFT was found to have better accuracy than both SIFT and SURF as the problem of false pairing is significantly reduced. We also propose a new method called S-SIFT where we used S Transform with SIFT to obtain the keypoint descriptor for the image and Phase-Only Correlation for the feature matching. In the thesis we provide a comparative analysis of these four methods (SIFT, SURF, F-SIFT, S-SIFT) for feature extraction in iris

Development of Robust Iris Localization and Impairment Pruning Schemes

Iris is the sphincter having flowery pattern around pupil in the eye region. The high randomness of the pattern makes iris unique for each individual and iris is identified by the scientists to be a candidate for automated machine recognition of identity of an individual. The morphogenesis of iris is completed while baby is in mother's womb; hence the iris pattern does not change throughout the span of life of a person. It makes iris one of the most reliable biometric traits. Localization of iris is the first step in iris biometric recognition system. The performance of matching is dependent on the accuracy of localization, because mislocalization would lead the next phases of biometric system to malfunction. The first part of the thesis investigates choke points of the existing localization approaches and proposes a method of devising an adaptive threshold of binarization for pupil detection. The thesis also contributes in modifying conventional integrodifferential operator based iris detection and proposes a modified version of it that uses canny detected edge map for iris detection. The other part of the thesis looks into pros and cons of the conventional global and local feature matching techniques for iris. The review of related research works on matching techniques leads to the observation that local features like Scale Invariant Feature Transform(SIFT) gives satisfactory recognition accuracy for good quality images. But the performance degrades when the images are occluded or taken non-cooperatively. As SIFT matches keypoints on the basis of 128-D local descriptors, hence it sometimes falsely pairs two keypoints which are from different portions of two iris images. Subsequently the need for filtering or pruning of faulty SIFT pairs is felt. The thesis proposes two methods of filtering impairments (faulty pairs) based on the knowledge of spatial information of the keypoints. The two proposed pruning algorithms (Angular Filtering and Scale Filtering) are applied separately and applied in union to have a complete comparative analysis of the result of matching

Parallel algorithms for iris biometrics

Iris biometrics involves preprocessing, feature extraction and identification phase. In this thesis,an effort has been made to introduce parallelism in feature extraction and identification phases. Local features invariant to scale, rotation, illumination are extracted using Scale Invariant Feature Transform (SIFT). In order to achieve speedup during feature extraction, parallelism has been introduced during scale space construction using SIMD hypercube. The parallel time complexity is O(N2) whereas sequential algorithm performs with complexity of O(lsN2, where l is the number of octaves, s is the number of Gaussian scale levels within an octave and N × N is the size of iris image

Improving Iris Recognition through Quality and Interoperability Metrics

The ability to identify individuals based on their iris is known as iris recognition. Over the past decade iris recognition has garnered much attention because of its strong performance in comparison with other mainstream biometrics such as fingerprint and face recognition. Performance of iris recognition systems is driven by application scenario requirements. Standoff distance, subject cooperation, underlying optics, and illumination are a few examples of these requirements which dictate the nature of images an iris recognition system has to process. Traditional iris recognition systems, dubbed stop and stare , operate under highly constrained conditions. This ensures that the captured image is of sufficient quality so that the success of subsequent processing stages, segmentation, encoding, and matching are not compromised. When acquisition constraints are relaxed, such as for surveillance or iris on the move, the fidelity of subsequent processing steps lessens.;In this dissertation we propose a multi-faceted framework for mitigating the difficulties associated with non-ideal iris. We develop and investigate a comprehensive iris image quality metric that is predictive of iris matching performance. The metric is composed of photometric measures such as defocus, motion blur, and illumination, but also contains domain specific measures such as occlusion, and gaze angle. These measures are then combined through a fusion rule based on Dempster-Shafer theory. Related to iris segmentation, which is arguably one of the most important tasks in iris recognition, we develop metrics which are used to evaluate the precision of the pupil and iris boundaries. Furthermore, we illustrate three methods which take advantage of the proposed segmentation metrics for rectifying incorrect segmentation boundaries. Finally, we look at the issue of iris image interoperability and demonstrate that techniques from the field of hardware fingerprinting can be utilized to improve iris matching performance when images captured from distinct sensors are involved

Reconhecimento biométrico considerando a deformação não linear da íris humana

The biometric systems that use the information on iris texture has received great attention in recent years. The extraordinary variation in iris texture allows the creation of recognition and identification systems with almost zero error rates. However, in general, researches ignore the problems associated with contraction and dilation iris movements that can result in significant differences between the enrollment images and the probe image. This work, in addition to developing a traditional iris recognition system, comprising the steps of detection, segmentation, normalization, encoding and comparison, determines quantitatively the iris motion effect in recognition system accuracy. In addition, this paper proposes a new method to reduce the influence of dynamic iris, verified by decidability and the Equal Error Rate (EER), obtained in the comparison between iris codes in very different expansion states. The new method uses Dynamic Time Warping technique to correct and compare the gradient vectors extracted from iris texture. Thus, the most discriminant features of the test image and enrollment image are aligned and compared, considering the non-linear distortion of the iris tissue. Experimental results using dynamic images indicate that system performance gets worse with comparison on images in different states contraction. For direct comparison with contracted and dilated iris the proposed method improves the decidability of 3.50 to 4.39 and EER of 9.69% to 3.36%.Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo a Pesquisa do Estado de Minas GeraisTese (Doutorado)Os sistemas biométricos que utilizam a informação contida na textura da íris têm recebido grande atenção nos últimos anos. A grande variação em textura da íris permite o desenvolvimento de sistemas de reconhecimento e de identificação com taxas de erro quase nulas. Entretanto, de forma geral, as pesquisas nesta área ignoram os problemas associados aos movimentos de contração e dilatação da íris que geram diferenças significativas entre as imagens inscritas em uma base de dados e a imagem de teste. Este trabalho, além de desenvolver um sistema de reconhecimento de íris tradicional, composto pelas etapas de detecção, segmentação, normalização, codificação e comparação, determina de forma quantitativa o efeito dos movimentos da íris na precisão do sistema de reconhecimento. Além disso, este trabalho propõe um novo método para diminuir a influência da dinâmica da íris, verificado pela decidibilidade e pela Taxa de Erro Igual (EER), obtidas na comparação entre códigos de íris em estados de dilatação bem diferentes. O novo método utiliza a técnica Dynamic Time Warping para corrigir e comparar os vetores de gradientes extraídos da textura da íris. Dessa forma, as características mais discriminantes da imagem de teste e da imagem da galeria são alinhadas e comparadas, considerando a deformação não linear do tecido da íris. Os resultados experimentais, utilizando imagens dinâmicas, indicam que a performance do sistema piora quando a comparação é feita com imagens em estados de contração diferentes. Para a comparação direta entre íris bem contraída com íris bem dilatada o método proposto melhora a decidibilidade de 3,50 para 4,39 e a EER de 9,69% para 3,36%

On the Performance Improvement of Iris Biometric System

Iris is an established biometric modality with many practical applications. Its performance is influenced by noise, database size, and feature representation. This thesis focusses on mitigating these challenges by efficiently characterising iris texture,developing multi-unit iris recognition, reducing the search space of large iris databases, and investigating if iris pattern change over time.To suitably characterise texture features of iris, Scale Invariant Feature Transform (SIFT) is combined with Fourier transform to develop a keypoint descriptor-F-SIFT. Proposed F-SIFT is invariant to transformation, illumination, and occlusion along with strong texture description property. For pairing the keypoints from gallery and probe iris images, Phase-Only Correlation (POC) function is used. The use of phase information reduces the wrong matches generated using SIFT. Results demonstrate the effectiveness of F-SIFT over existing keypoint descriptors.To perform the multi-unit iris fusion, a novel classifier is proposed known as Incremental Granular Relevance Vector Machine (iGRVM) that incorporates incremental and granular learning into RVM. The proposed classifier by design is scalable and unbiased which is particularly suitable for biometrics. The match scores from individual units of iris are passed as an input to the corresponding iGRVM classifier, and the posterior probabilities are combined using weighted sum rule. Experimentally, it is shown that the performance of multi-unit iris recognition improves over single unit iris. For search space reduction, local feature based indexing approaches are developed using multi-dimensional trees. Such features extracted from annular iris images are used to index the database using k-d tree. To handle the scalability issue of k-d tree, k-d-b tree based indexing approach is proposed. Another indexing approach using R-tree is developed to minimise the indexing errors. For retrieval, hybrid coarse-to-fine search strategy is proposed. It is inferred from the results that unification of hybrid search with R-tree significantly improves the identification performance. Iris is assumed to be stable over time. Recently, researchers have reported that false rejections increase over the period of time which in turn degrades the performance. An empirical investigation has been made on standard iris aging databases to find whether iris patterns change over time. From the results, it is found that the rejections are primarily due to the presence of other covariates such as blur, noise, occlusion, pupil dilation, and not due to agin

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