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A Novel Approach to Iris Localization and Code Matching for Iris RecognitionZhou, Steven 01 January 2009 (has links)
In recent years, computing power and biometric sensors have not only become more powerful, but also more affordable to the general public. In turn, there has been great interest in developing and deploying biometric personal ID systems. Unlike the conventional security systems that often require people to provide artificial identification for verification, i.e. password or algorithmic generated keys, biometric security systems use an individual's biometric measurements, including fingerprint, face, hand geometry, and iris. It is believed that these measurements are unique to the individual, making them much more reliable and less likely to be stolen, lost, forgotten, or forged.
Among these biometric measurements, the iris is regarded as one of the most reliable and accurate security approaches because it is an internal organ protected by the body's own biological mechanisms. It is easy to access, and almost impossible to modify without the risk of damaging the iris.
Although there have been significant advancements in developing iris-based identification processes during recent years, there remains significant room for improvement. This dissertation presents a novel approach to the iris localization and code matching. It uses a fixed diameter method and a parabolic curve fitting approach for locating the iris and eyelids as well as a k-d tree for iris matching. The iris recognition rate is improved by accurately locating the eyelids and eliminating the signal noise in an eye image. Furthermore, the overall system performance is increased significantly by using a partial iris image and taking the advantage of the k-d binary tree.
We present the research results of four processing stages of iris recognition: localization, normalization, feature extraction, and code matching. The localization process is based on histogram analysis, morphological process, Canny edge detection, and parabolic curve fitting. The normalization process adopts Daugman's rubber-sheet approach and converts the iris image from Cartesian coordinators to polar coordinates. In the feature extraction process, the feature vectors are created and quantized using 1-D Log-Gabor wavelet. Finally, the iris code matching process is conducted using a k-dimensional binary tree and Hamming distance.
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Výzkum systému GPS pro lokalizaci bezdrátových senzorových uzlů / Research into GPS system used for Wireless Sensor Node LocalizationJuračka, Jan January 2013 (has links)
Theme of the thesis is research and possibility of using GPS system from localization in wireless sensor network. Paper deals with the accuracy and energy consumption of GPS localization. Thesis also solve using of localization in local anchor system. Theoretical part describes IEEE 802.15.4 standard, capability of used nodes and describe ways how to use RSSI value to resolve location
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Výzkum systému GPS pro lokalizaci bezdrátových senzorových uzlů / Research into GPS system used for Wireless Sensor Node LocalizationJuračka, Jan January 2013 (has links)
Theme of the thesis is research and possibility of using GPS system from localization in wireless sensor network. Paper deals with the accuracy and energy consumption of GPS localization. Thesis also solve using of localization in local anchor system. Theoretical part describes IEEE 802.15.4 standard, capability of used nodes and describe ways how to use RSSI value to resolve location
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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.Nassar, Alaa S.N. January 2018 (has links)
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 Iraq
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A Fast and Accurate Iris Localization Technique for Healthcare Security SystemAl-Waisy, Alaa S., Qahwaji, Rami S.R., Ipson, Stanley S., Al-Fahdawi, Shumoos January 2015 (has links)
Yes / In 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.
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Reconhecimento de íris utilizando algoritmos genéticos e amostragem não uniforme / Iris Recognition using Genetic Algorithms and Non- Uniform Sampling,Carneiro, Milena Bueno Pereira 06 December 2010 (has links)
The automatic recognition of individuals through the iris characteristics
is an e±cient biometric technique that is widely studied and applied around
the world. Many image processing stages are necessary to make possible
the representation and the interpretation of the iris information. This work
presents the state of the art in iris recognition systems where the most re-
markable works and the di®erent techniques applied to perform each process-
ing stage are quoted. The implementations of each processing stage using
traditional techniques are presented and, afterwards, two innovator methods
are proposed with the common objective of bringing bene¯t to the system.
The ¯rst processing stage should be the localization of the iris region in an
eye image. The ¯rst method proposed in this work presents an algorithm
to achieve the iris localization through the utilization of the called Memetic
Algorithms. The new method is compared to a classical method and the
obtained results show advantages concerning e±ciency and processing time.
In another processing stage there must be a pixels sampling from the iris
region, from where the information used to di®erentiate the individuals is
extracted. Traditionally, this sampling process is accomplished in an uni-
form way along the whole iris region. It is proposed a pre-processing method
which suggests a non uniform pixels sampling from the iris region with the
objective of selecting the group of pixels which carry more information about
the iris structure. The search for this group of pixels is done through Ge-
netic Algorithms. The application of the new method improves the e±ciency
of the system and also, allows the generation of smaller templates. In this
work, a study on the called Active Shape Models is also accomplished and its
application to perform the iris region segmentation is evaluated. To execute
the simulations and the evaluation of the methods, it was used two public
and free iris images database: UBIRIS database and MMU database. / O reconhecimento automático de pessoas utilizando-se características da íris é uma eficiente técnica biométrica que está sendo largamente estudada e aplicada em todo o mundo. Diversas etapas de processamento são necessárias para tornar possível a representação e a interpretação da informação contida na íris. Neste trabalho é apresentado o estado da arte de sistemas de reconhecimento de íris onde são citados os trabalhos de maior destaque e as diferentes técnicas empregadas em cada etapa de processamento. São apresentadas implementações de cada etapa de processamento utilizando técnicas tradicionais e, posteriormente, são propostos dois métodos inovadores que têm o objetivo comum de trazer benefícios ao sistema. A primeira etapa de processamento é a localização da região da íris na imagem. O primeiro método proposto neste trabalho apresenta um algoritmo para realizar a localização da íris utilizando os chamados Algoritmos Meméticos. O novo método é comparado a um método clássico e os resultadosnobtidos demonstram vantagens no que diz respeito à eficiência e ao tempo de processamento. Em uma outra etapa de processamento deve haver uma amostragem de pixels na região da íris, de onde são retiradas as informações utilizadas para diferenciar os indivíduos. Tradicionalmente, esta amostragem é realizada de maneira uniforme ao longo de toda a região da íris. É proposto um método de pré-processamento que sugere uma amostragem não uniforme de pixels na região da íris com o objetivo de selecionar o conjunto de pixels que carregam mais informações da estrutura da íris. A busca por esse conjunto de pixels é realizada utilizando-se Algoritmos Genéticos. A aplicação deste novo método aumenta a eficiência do sistema e ainda possibilita a geração de templates binários menores. Neste trabalho é realizado, ainda, um estudos dos chamados Active Shape Models e a sua aplicação para segmentar a região da íris é avaliada. Para a simulação e avaliação dos métodos, foram utilizados dois bancos de imagens de íris públicos e gratuitos: o banco de imagens UBIRIS e o banco de imagens MMU. / Doutor em Ciências
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