11 research outputs found
Separability Filter for Localizing Abnormal Pupil: Identification of Input Image
Separability filter method is a reliable method for pupil detection. However, so far this method is implemented for detecting pupil of normal eye, while for abnormal eye such as cataract and glaucoma patients; they have different characteristics of pupil such as color, shape and radius size of pupil. In this paper we propose to use separability filter for detecting pupil of abnormal patients with different characteristics. We faced a problem about radius size, shape and color of pupil; therefore we implemented Hough Transform, Blob area and Brightness for identifying input images before applying separability filter. The experiment results show that we can increase performance of pupil detection for abnormal eye to be 95.65%
Efficient Iris Localization via Optimization Model
Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method) algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square) is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective
Optimized biometric system based iris-signature for human identification
This research aimed at comparing iris-signature techniques, namely the Sequential Technique (ST) and the Standard Deviation Technique (SDT). Both techniques were measured by Backpropagation (BP), Probabilistic, Radial basis function (RBF), and Euclidian distance (ED) classifiers. A biometric system-based iris is developed to identify 30 of CASIA-v1 and 10 subjects from the Real-iris datasets. Then, the proposed unimodal system uses Fourier descriptors to extract the iris features and represent them as an iris-signature graph. The 150 values of input machine vector were optimized to include only high-frequency coefficients of the iris-signature, then the two optimization techniques are applied and compared. The first optimization (ST) selects sequentially new feature values with different lengths from the enrichment graph region that has rapid frequency changes. The second technique (SDT) chooses the high variance coefficients as a new feature of vectors based on the standard deviation formula. The results show that SDT achieved better recognition performance with the lowest vector-lengths, while Probabilistic and BP have the best accuracy
Concepção de um sistema alternativo de reconhecimento de íris cooperativo
Nos dias de hoje, uma das mais importantes condições que está associado ao ser humano é a segurança. Cada vez mais se pretende garantir a autenticidade das pessoas evitando assim ataques e invasões maliciosas. É nesse contexto que surgem os sistemas biométricos, como forma de solucionar esses problemas. Mais concretamente, o uso da íris como medida biométrica, tem sido dos métodos mais
promissores, completos e robustos existentes no mercado. As suas aplicações são vastas, desde à utilização em aeroportos, laboratórios, bancos ou prisões. Em todos
estes exemplos, é necessária uma cooperação dos indivíduos que permite adquirir
imagens de qualidade para o processo de reconhecimento.
Com a utilização de um sistema biométrico, independentemente da característica
fisiológica utilizada, existem dois tipos de identificação: verificar se uma boa é quem
diz ser ou identificar a pessoa em questão dizendo concretamente de quem se trata,
caso essa seja uma das pessoas com autorização. Sendo que as características
físicas funcionam como senha de acesso, os comuns problemas de esquecimento de
passwords ou de furto de cartões de acesso deixam de fazer sentido. As pessoas são
a sua própria senha.
Nesta tese, encontra-se descriminado as metodologias que visam responder às várias
etapas do reconhecimento da íris. No entanto, os métodos apresentados, tentam contornar a patente criada por John Daugman em 1994. É a única patente utilizada nos sistemas de reconhecimento biométrico em comercialização através da íris. Os métodos consistem inicialmente na segmentação da íris em imagens capturadas. De seguida, as imagens segmentadas passam por uma fase de normalização para um melhor manuseamento dos dados. Por fim existem métodos que determinam quais os valores mais aptos para extrair informação e criar uma assinatura biométrica. Os diversos métodos propostos encontram-se complementados com resultados, que
justificam as várias decisões tomadas.Nowadays, one of the most relevant conditions associated to the human being is
security. The preservation of people authenticity to avoid attacks and malicious
invasions are increasing. The appearance of biometric systems work as a way to
solve these issues. Specifically, the usage of iris as a biometric trait incorporated in
a biometric system has been the most promising, complete and robust that can be
offered. There are several applications like airports, laboratories, banks and prisons.
In all of these examples, the subject cooperation with the devices is required allowing
the capture of good quality images to the recognition process.
With the usage of a biometric system, regardless the physical feature, there
are two types of identifications: check if someone is who he says he is or identify the
subject by giving his own identity, if that person is considered an authorized subject.
Considering that the physical traits work as an “allow permission”, the usual issues
associated to the forgotten passwords or the stolen identity cards no longer make
sense. People are they own password.
In this thesis are described several methodologies that respond to all of the iris
recognition steps. However, the proposed methods try to circumvent the patent
created by John Daugman in 1994. This patent is the only one used in the iris
biometric recognition system in the market. Inicially, the proposed methods work
on the iris segmentation. Then, the segmented images go to the normalization
step for a better data manipulation. To conclude, there are methods to determine
witch are the best settings to feature extraction to biometric signature creation. The
several proposed methods are accomplished with test results that justify the token
decisions
Motion Segmentation from Clustering of Sparse Point Features Using Spatially Constrained Mixture Models
Motion is one of the strongest cues available for segmentation. While motion segmentation finds wide ranging applications in object detection, tracking, surveillance, robotics, image and video compression, scene reconstruction, video editing, and so on, it faces various challenges such as accurate motion recovery from noisy data, varying complexity of the models required to describe the computed image motion, the dynamic nature of the scene that may include a large number of independently moving objects undergoing occlusions, and the need to make high-level decisions while dealing with long image sequences. Keeping the sparse point features as the pivotal point, this thesis presents three distinct approaches that address some of the above mentioned motion segmentation challenges. The first part deals with the detection and tracking of sparse point features in image sequences. A framework is proposed where point features can be tracked jointly. Traditionally, sparse features have been tracked independently of one another. Combining the ideas from Lucas-Kanade and Horn-Schunck, this thesis presents a technique in which the estimated motion of a feature is influenced by the motion of the neighboring features. The joint feature tracking algorithm leads to an improved tracking performance over the standard Lucas-Kanade based tracking approach, especially while tracking features in untextured regions. The second part is related to motion segmentation using sparse point feature trajectories. The approach utilizes a spatially constrained mixture model framework and a greedy EM algorithm to group point features. In contrast to previous work, the algorithm is incremental in nature and allows for an arbitrary number of objects traveling at different relative speeds to be segmented, thus eliminating the need for an explicit initialization of the number of groups. The primary parameter used by the algorithm is the amount of evidence that must be accumulated before the features are grouped. A statistical goodness-of-fit test monitors the change in the motion parameters of a group over time in order to automatically update the reference frame. The approach works in real time and is able to segment various challenging sequences captured from still and moving cameras that contain multiple independently moving objects and motion blur. The third part of this thesis deals with the use of specialized models for motion segmentation. The articulated human motion is chosen as a representative example that requires a complex model to be accurately described. A motion-based approach for segmentation, tracking, and pose estimation of articulated bodies is presented. The human body is represented using the trajectories of a number of sparse points. A novel motion descriptor encodes the spatial relationships of the motion vectors representing various parts of the person and can discriminate between articulated and non-articulated motions, as well as between various pose and view angles. Furthermore, a nearest neighbor search for the closest motion descriptor from the labeled training data consisting of the human gait cycle in multiple views is performed, and this distance is fed to a Hidden Markov Model defined over multiple poses and viewpoints to obtain temporally consistent pose estimates. Experimental results on various sequences of walking subjects with multiple viewpoints and scale demonstrate the effectiveness of the approach. In particular, the purely motion based approach is able to track people in night-time sequences, even when the appearance based cues are not available. Finally, an application of image segmentation is presented in the context of iris segmentation. Iris is a widely used biometric for recognition and is known to be highly accurate if the segmentation of the iris region is near perfect. Non-ideal situations arise when the iris undergoes occlusion by eyelashes or eyelids, or the overall quality of the segmented iris is affected by illumination changes, or due to out-of-plane rotation of the eye. The proposed iris segmentation approach combines the appearance and the geometry of the eye to segment iris regions from non-ideal images. The image is modeled as a Markov random field, and a graph cuts based energy minimization algorithm is applied to label the pixels either as eyelashes, pupil, iris, or background using texture and image intensity information. The iris shape is modeled as an ellipse and is used to refine the pixel based segmentation. The results indicate the effectiveness of the segmentation algorithm in handling non-ideal iris images
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%
Biometric iris image segmentation and feature extraction for iris recognition
PhD ThesisThe continued threat to security in our interconnected world today begs for urgent
solution. Iris biometric like many other biometric systems provides an alternative solution
to this lingering problem. Although, iris recognition have been extensively studied, it is
nevertheless, not a fully solved problem which is the factor inhibiting its implementation
in real world situations today. There exists three main problems facing the existing iris
recognition systems: 1) lack of robustness of the algorithm to handle non-ideal iris
images, 2) slow speed of the algorithm and 3) the applicability to the existing systems in
real world situation. In this thesis, six novel approaches were derived and implemented
to address these current limitation of existing iris recognition systems.
A novel fast and accurate segmentation approach based on the combination of graph-cut
optimization and active contour model is proposed to define the irregular boundaries of
the iris in a hierarchical 2-level approach. In the first hierarchy, the approximate boundary
of the pupil/iris is estimated using a method based on Hough’s transform for the pupil and
adapted starburst algorithm for the iris. Subsequently, in the second hierarchy, the final
irregular boundary of the pupil/iris is refined and segmented using graph-cut based active
contour (GCBAC) model proposed in this work. The segmentation is performed in two
levels, whereby the pupil is segmented first before the iris. In order to detect and eliminate
noise and reflection artefacts which might introduce errors to the algorithm, a preprocessing
technique based on adaptive weighted edge detection and high-pass filtering
is used to detect reflections on the high intensity areas of the image while exemplar based
image inpainting is used to eliminate the reflections. After the segmentation of the iris
boundaries, a post-processing operation based on combination of block classification
method and statistical prediction approach is used to detect any super-imposed occluding
eyelashes/eyeshadows. The normalization of the iris image is achieved though the rubber
sheet model.
In the second stage, an approach based on construction of complex wavelet filters and
rotation of the filters to the direction of the principal texture direction is used for the
extraction of important iris information while a modified particle swam optimization
(PSO) is used to select the most prominent iris features for iris encoding. Classification
of the iriscode is performed using adaptive support vector machines (ASVM).
Experimental results demonstrate that the proposed approach achieves accuracy of
98.99% and is computationally about 2 times faster than the best existing approach.Ebonyi State
University and Education Task Fund, Nigeri