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[en] AN IDENTIFICATION SYSTEM BASED ON IRIS STRUCTURE ANALYSIS / [pt] SISTEMA DE IDENTIFICAÇÃO BASEADA NA ESTRUTURA DA ÍRISRODRIGO DA COSTA NASCIMENTO 28 December 2005 (has links)
[pt] O reconhecimento de humanos pela íris é um dos sistemas
mais seguros de
identificação biométrica e, motivou a construção de um
protótipo de identificação humana
baseada na estrutura da íris. O sistema construído é
composto de um dispositivo de captura
de imagens da íris humana e algoritmos para pré-
processamento da imagem, para a
representação e o reconhecimento. Cada um dos elementos
que compõem o protótipo são
avaliados a partir de dois bancos de dados de imagens de
íris. Os resultados demonstraram
que o dispositivo proposto e os modelos apresentados são
capazes de realizar o
reconhecimento humano através da íris de forma eficiente. / [en] The recognition of human beings for the Iris is one of the
safest systems of biometric
identification. This motivated the construction of a
prototype for identification of human
beings based on the structure of the Iris. The constructed
system is composed of a device
capable to capture images of the Iris and algorithms for
image pre - processing, for the
representation and recognition each element composing the
prototype is evaluated using
two data bases of Iris images. The results have
demonstrated that the prototype and the
presented models are capable to efficiently identify the
human based on Iris structure.
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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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Algorithms to Process and Measure Biometric Information Content in Low Quality Face and Iris ImagesYoumaran, Richard January 2011 (has links)
Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.
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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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Biometrická brána využívající kamer pro identifikaci osob / Biometric Gateway Using Camera to Identify PeopleJelen, Vilém January 2019 (has links)
Biometric gateways are used to quickly and accurately identify people. Of the biometric characteristics, iris, face and fingerprints are commonly used. By combining them, better identification results can be achieved. The aim of this thesis is to create such a biometric gateway together with the control application. A combination of iris of both eyes and face is used, which is captured by cameras from three angles to increase accuracy. Neural networks are used to detect and extract face features. Iris recognition is realized using Daugman's algorithm.
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Biometrie s využitím snímků duhovky / Biometry based on iris imagesTobiášová, Nela January 2014 (has links)
The biometric techniques are well known and widespread nowadays. In this context biometry means automated person recognition using anatomic features. This work uses the iris as the anatomic feature. Iris recognition is taken as the most promising technique of all because of its non-invasiveness and low error rate. The inventor of iris recognition is John G. Daugman. His work underlies almost all current public works of this technology. This final thesis is concerned with biometry based on iris images. The principles of biometric methods based on iris images are described in the first part. The first practical part of this work is aimed at the proposal and realization of two methods which localize the iris inner boundary. The third part presents the proposal and realization of iris image processing in order to classifying persons. The last chapter is focus on evaluation of experimental results and there are also compared our results with several well-known methods.
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Software pro biometrické rozpoznávání duhovky lidského oka / Software for Biometric Recognition of a Human Eye IrisMaruniak, Lukáš January 2015 (has links)
In my thesis, I focus on the task of recognizing human iris from an image.In the beginning, the work deals with a question of biometrics, its importance and basic concepts, which are necessary for use in following text. Subsequently process of human Iris detection is described together with theory of evolution algorithms. In the implementation part, is described the design of implemented solution, which uses evolution algorithms, where is emphasis on correct pupil and iris boundary detection.
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