7 research outputs found
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
vii
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
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
iii
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
Handbook of Vascular Biometrics
This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port