34 research outputs found
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
Segmentação de faces em imagens no infravermelho térmico
O objectivo desta dissertação é desenvolver um método para a segmentação automática
de faces em imagens capturadas no infravermelho térmico, permitindo uma
ampla gama de rotações da face e expressões. A motivação por trás desse esforço
é de possibilitar um melhor desempenho dos métodos de reconhecimento de faces
em imagens no infravermelho térmico. Ao longo desta dissertação são discutidos
oito abordagens diferentes e a comparação dos seus desempenhos com outros três
métodos publicados anteriormente. As abordagens propostas são baseadas em modelos
estatÃsticos das intensidades dos pixéis e a aplicação de contornos activos, contudo
outras operações de processamento de imagem são realizadas. Estudamos também o
desempenho de três abordagens de fusão sob diferentes regras (votação por maioria,
operador lógico AND e OR). As experiências foram realizadas num total de 893
imagens de teste de 4 bases de dados públicas disponÃveis. Os resultados obtidos
melhoram os resultados dos métodos existentes até 31:2% para a primeira medida de
erro (E1) e até 39:0% para a segunda medida (E2), dependendo do método e da base
de dados. Quanto ao tempo computacional, as nossas propostas podem melhorar até
75:4% quando comparadas com as outras propostas
Automatic segmentation of anterior segment optical coherence tomography images
Automatic segmentation of anterior segment optical coherence tomography (AS OCT) images provides an important tool to aid management of ocular diseases. Having precise details about the topography and thickness of an individual eye enables treatments to be tailored to a specific problem. OCT is an imaging technique that can be used to acquire volumetric data of the anterior segment of the human eye. Fast automatic segmentation of this data, which is not available, means clinically useful information can be obtained without the need for time consuming error-prone manual analysis of the images. This thesis presents newly developed automatic segmentation techniques of OCT images. Segmentation of 2D OCT images is first performed. One of the main challenges segmenting 2D OCT images is the presence of regions of the image that generally have a low signal to noise ratio. This is overcome by the use of shape based terms. A number of different methods, such as level set, graph cut, and graph theory, are developed to do this. The segmentation techniques are validated by comparison to expert manual segmentation and previously published segmentation techniques. The best method, graph theory with shape, was able to achieve segmentation comparable to manual segmentation. Good agreement is found with manual segmentation for the best 2D segmentation method, graph theory with shape, achieving a Dice similarity coefficient of 0.96, which is comparable to inter-observer agreement. It performed significantly better than previously published techniques. The 2D segmentation techniques are then extended to 3D segmentation of OCT images. The challenge here is motion artefact or poor alignment between each 2D images comprising the 3D images. Different segmentation strategies are investigated including direct segmentation by level set or graph cut approaches, and segmentation with registration. In particular the latter requires the introduction of a registration step to align multiple 2D images to produce a 3D representation to overcome the presence of involuntary motion artefacts. This method produces the best performance. In particular, it uses graph theory and dynamic programming to segment the anterior and posterior surfaces in individual 2D images with shape constraint. Genetic algorithms are then used to align 2D images to produce a full 3D representation of the anterior segment based on landmarks or geometric constraints. For the 3D segmentation, a data set of 17 eyes is used for validation. These have each been imaged twice so a repeatability measurement can be made. Good repeatability of results is demonstrated with the 3D alignment method. A mean difference of 1.77 pixels is found between the same surfaces of the repeated scans of the same eye. Overall, a new automation method is developed that can produce maps of the anterior and posterior surfaces of the cornea from a 3D images of the anterior segment of a human eye. This will be a valuable tool that can be used for patient specific biomechanical modelling of the human eye
Recognition of Nonideal Iris Images Using Shape Guided Approach and Game Theory
Most state-of-the-art iris recognition algorithms claim to perform with a very high recognition accuracy in a strictly controlled environment. However, their recognition accuracies significantly decrease when the acquired images are affected by different noise factors including motion blur, camera diffusion, head movement, gaze direction, camera angle, reflections, contrast, luminosity, eyelid and eyelash occlusions, and problems due to contraction and dilation. The main objective of this thesis is to develop a nonideal iris recognition system by using active contour methods, Genetic Algorithms (GAs), shape guided model, Adaptive Asymmetrical Support Vector Machines (AASVMs) and Game Theory (GT). In this thesis, the proposed iris recognition method is divided into two phases: (1) cooperative iris recognition, and (2) noncooperative iris recognition.
While most state-of-the-art iris recognition algorithms have focused on the preprocessing of iris images, recently, important new directions have been identified in iris biometrics research. These include optimal feature selection and iris pattern classification. In the first phase, we propose an iris recognition scheme based on GAs and asymmetrical SVMs. Instead of using the whole iris region, we elicit the iris information between the collarette and the pupil boundary to suppress the effects of eyelid and eyelash occlusions and to minimize the matching error.
In the second phase, we process the nonideal iris images that are captured in unconstrained situations and those affected by several nonideal factors. The proposed noncooperative iris recognition method is further divided into three approaches.
In the first approach of the second phase, we apply active contour-based curve evolution approaches to segment the inner/outer boundaries accurately from the nonideal iris images. The proposed active contour-based approaches show a reasonable performance when the iris/sclera boundary is separated by a blurred boundary. In the second approach, we describe a new iris segmentation scheme using GT to elicit iris/pupil boundary from a nonideal iris image. We apply a parallel game-theoretic decision making procedure by modifying Chakraborty and Duncan's algorithm to form a unified approach, which is robust to noise and poor localization and less affected by weak iris/sclera boundary. Finally, to further improve the segmentation performance, we propose a variational model to localize the iris region belonging to the given shape space using active contour method, a geometric shape prior and the Mumford-Shah functional.
The verification and identification performance of the proposed scheme is validated using four challenging nonideal iris datasets, namely, the ICE 2005, the UBIRIS Version 1, the CASIA Version 3 Interval, and the WVU Nonideal, plus the non-homogeneous combined dataset. We have conducted several sets of experiments and finally, the proposed approach has achieved a Genuine Accept Rate (GAR) of 97.34% on the combined dataset at the fixed False Accept Rate (FAR) of 0.001% with an Equal Error Rate (EER) of 0.81%. The highest Correct Recognition Rate (CRR) obtained by the proposed iris recognition system is 97.39%
Advancing iris biometric technology
PhD ThesisThe iris biometric is a well-established technology which is already in use in
several nation-scale applications and it is still an active research area with several
unsolved problems. This work focuses on three key problems in iris biometrics
namely: segmentation, protection and cross-matching. Three novel
methods in each of these areas are proposed and analyzed thoroughly.
In terms of iris segmentation, a novel iris segmentation method is designed
based on a fusion of an expanding and a shrinking active contour by integrating
a new pressure force within the Gradient Vector Flow (GVF) active
contour model. In addition, a new method for closed eye detection is proposed.
The experimental results on the CASIA V4, MMU2, UBIRIS V1 and
UBIRIS V2 databases show that the proposed method achieves state-of-theart
results in terms of segmentation accuracy and recognition performance
while being computationally more efficient. In this context, improvements
by 60.5%, 42% and 48.7% are achieved in segmentation accuracy for the
CASIA V4, MMU2 and UBIRIS V1 databases, respectively. For the UBIRIS
V2 database, a superior time reduction is reported (85.7%) while maintaining
a similar accuracy. Similarly, considerable time improvements by 63.8%,
56.6% and 29.3% are achieved for the CASIA V4, MMU2 and UBIRIS V1
databases, respectively.
With respect to iris biometric protection, a novel security architecture is designed
to protect the integrity of iris images and templates using watermarking
and Visual Cryptography (VC). Firstly, for protecting the iris image, text
which carries personal information is embedded in the middle band frequency
region of the iris image using a novel watermarking algorithm that randomly
interchanges multiple middle band pairs of the Discrete Cosine Transform
(DCT). Secondly, for iris template protection, VC is utilized to protect the
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iris template. In addition, the integrity of the stored template in the biometric
smart card is guaranteed by using the hash signatures. The proposed method
has a minimal effect on the iris recognition performance of only 3.6% and
4.9% for the CASIA V4 and UBIRIS V1 databases, respectively. In addition,
the VC scheme is designed to be readily applied to protect any biometric binary
template without any degradation to the recognition performance with a
complexity of only O(N).
As for cross-spectral matching, a framework is designed which is capable of
matching iris images in different lighting conditions. The first method is designed
to work with registered iris images where the key idea is to synthesize
the corresponding Near Infra-Red (NIR) images from the Visible Light (VL)
images using an Artificial Neural Network (ANN) while the second method
is capable of working with unregistered iris images based on integrating the
Gabor filter with different photometric normalization models and descriptors
along with decision level fusion to achieve the cross-spectral matching. A
significant improvement by 79.3% in cross-spectral matching performance is
attained for the UTIRIS database. As for the PolyU database, the proposed
verification method achieved an improvement by 83.9% in terms of NIR vs
Red channel matching which confirms the efficiency of the proposed method.
In summary, the most important open issues in exploiting the iris biometric
are presented and novel methods to address these problems are proposed.
Hence, this work will help to establish a more robust iris recognition system
due to the development of an accurate segmentation method working for iris
images taken under both the VL and NIR. In addition, the proposed protection
scheme paves the way for a secure iris images and templates storage.
Moreover, the proposed framework for cross-spectral matching will help to
employ the iris biometric in several security applications such as surveillance
at-a-distance and automated watch-list identification.Ministry of Higher Education and
Scientific Research in Ira
Mixing Biometric Data For Generating Joint Identities and Preserving Privacy
Biometrics is the science of automatically recognizing individuals by utilizing biological traits such as fingerprints, face, iris and voice. A classical biometric system digitizes the human body and uses this digitized identity for human recognition. In this work, we introduce the concept of mixing biometrics. Mixing biometrics refers to the process of generating a new biometric image by fusing images of different fingers, different faces, or different irises. The resultant mixed image can be used directly in the feature extraction and matching stages of an existing biometric system. In this regard, we design and systematically evaluate novel methods for generating mixed images for the fingerprint, iris and face modalities. Further, we extend the concept of mixing to accommodate two distinct modalities of an individual, viz., fingerprint and iris. The utility of mixing biometrics is demonstrated in two different applications. The first application deals with the issue of generating a joint digital identity. A joint identity inherits its uniqueness from two or more individuals and can be used in scenarios such as joint bank accounts or two-man rule systems. The second application deals with the issue of biometric privacy, where the concept of mixing is used for de-identifying or obscuring biometric images and for generating cancelable biometrics. Extensive experimental analysis suggests that the concept of biometric mixing has several benefits and can be easily incorporated into existing biometric systems
Iris Recognition: Robust Processing, Synthesis, Performance Evaluation and Applications
The popularity of iris biometric has grown considerably over the past few years. It has resulted in the development of a large number of new iris processing and encoding algorithms. In this dissertation, we will discuss the following aspects of the iris recognition problem: iris image acquisition, iris quality, iris segmentation, iris encoding, performance enhancement and two novel applications.;The specific claimed novelties of this dissertation include: (1) a method to generate a large scale realistic database of iris images; (2) a crosspectral iris matching method for comparison of images in color range against images in Near-Infrared (NIR) range; (3) a method to evaluate iris image and video quality; (4) a robust quality-based iris segmentation method; (5) several approaches to enhance recognition performance and security of traditional iris encoding techniques; (6) a method to increase iris capture volume for acquisition of iris on the move from a distance and (7) a method to improve performance of biometric systems due to available soft data in the form of links and connections in a relevant social network
Advanced image processing techniques for detection and quantification of drusen
Dissertation presented to obtain the degree of Doctor of Philosophy in Electrical Engineering, speciality on Perceptional Systems, by the Universidade Nova de Lisboa,
Faculty of Sciences and TechnologyDrusen are common features in the ageing macula, caused by accumulation of extracellular materials beneath the retinal surface, visible in retinal fundus images as yellow spots.
In the ophthalmologists’ opinion, the evaluation of the total drusen area, in a sequence of images taken during a treatment, will help to understand the disease progression and effectiveness. However, this evaluation is fastidious and difficult to reproduce when performed manually.
A literature review on automated drusen detection showed that the works already
published were limited to techniques of either adaptive or global thresholds which showed a tendency to produce a significant number of false positives. The purpose for this work was to propose an alternative method to automatically quantify drusen using advanced digital image processing techniques.
This methodology is based on a detection and modelling algorithm to automatically
quantify drusen. It includes an image pre-processing step to correct the uneven illumination by using smoothing splines fitting and to normalize the contrast. To quantify drusen a detection and modelling algorithm is adopted. The detection uses a new gradient based segmentation algorithm that isolates drusen and provides basic drusen characterization to the modelling stage. These are then fitted by Gaussian functions, to produce a model of the
image, which is used to compute the affected areas.
To validate the methodology, two software applications, one for semi-automated
(MD3RI) and other for automated detection of drusen (AD3RI), were implemented. The first
was developed for Ophthalmologists to manually analyse and mark drusen deposits, while the other implemented algorithms for automatic drusen quantification.Four studies to assess the methodology accuracy involving twelve specialists have
taken place. These compared the automated method to the specialists and evaluated its
repeatability. The studies were analysed regarding several indicators, which were based on the
total affected area and on a pixel-to-pixel analysis. Due to the high variability among the
graders involved in the first study, a new evaluation method, the Weighed Matching Analysis,
was developed to improve the pixel-to-pixel analysis by using the statistical significance of
the observations to differentiate positive and negative pixels.
From the results of these studies it was concluded that the methodology proposed is
capable to automatically measure drusen in an accurate and reproducible process. Also, the thesis proposes new image processing algorithms, for image pre-processing, image
segmentation,image modelling and images comparison, which are also applicable to other image processing fields
Advancing the technology of sclera recognition
PhD ThesisEmerging biometric traits have been suggested recently to overcome
some challenges and issues related to utilising traditional human
biometric traits such as the face, iris, and fingerprint. In particu-
lar, iris recognition has achieved high accuracy rates under Near-
InfraRed (NIR) spectrum and it is employed in many applications for
security and identification purposes. However, as modern imaging
devices operate in the visible spectrum capturing colour images, iris
recognition has faced challenges when applied to coloured images
especially with eye images which have a dark pigmentation. Other
issues with iris recognition under NIR spectrum are the constraints on
the capturing process resulting in failure-to-enrol, and degradation in
system accuracy and performance. As a result, the research commu-
nity investigated using other traits to support the iris biometric in the
visible spectrum such as the sclera.
The sclera which is commonly known as the white part of the eye
includes a complex network of blood vessels and veins surrounding
the eye. The vascular pattern within the sclera has different formations
and layers providing powerful features for human identification. In
addition, these blood vessels can be acquired in the visible spectrum
and thus can be applied using ubiquitous camera-based devices. As a
consequence, recent research has focused on developing sclera recog-
nition. However, sclera recognition as any biometric system has issues
and challenges which need to be addressed. These issues are mainly
related to sclera segmentation, blood vessel enhancement, feature ex-
traction, template registration, matching and decision methods. In
addition, employing the sclera biometric in the wild where relaxed
imaging constraints are utilised has introduced more challenges such
as illumination variation, specular reflections, non-cooperative user
capturing, sclera blocked region due to glasses and eyelashes, variation
in capturing distance, multiple gaze directions, and eye rotation.
The aim of this thesis is to address such sclera biometric challenges
and highlight the potential of this trait. This also might inspire further
research on tackling sclera recognition system issues. To overcome the
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above-mentioned issues and challenges, three major contributions are
made which can be summarised as 1) designing an efficient sclera
recognition system under constrained imaging conditions which in-
clude new sclera segmentation, blood vessel enhancement, vascular
binary network mapping and feature extraction, and template registra-
tion techniques; 2) introducing a novel sclera recognition system under
relaxed imaging constraints which exploits novel sclera segmentation,
sclera template rotation alignment and distance scaling methods, and
complex sclera features; 3) presenting solutions to tackle issues related
to applying sclera recognition in a real-time application such as eye
localisation, eye corner and gaze detection, together with a novel image
quality metric.
The evaluation of the proposed contributions is achieved using five
databases having different properties representing various challenges
and issues. These databases are the UBIRIS.v1, UBIRIS.v2, UTIRIS,
MICHE, and an in-house database. The results in terms of segmen-
tation accuracy, Equal Error Rate (EER), and processing time show
significant improvement in the proposed systems compared to state-
of-the-art methods.Ministry of Higher Education and
Scientific Research in Iraq and the Iraqi Cultural Attach´e in Londo