14,909 research outputs found
Exploring New Directions in Iris Recognition
A new approach in iris recognition based on Circular Fuzzy Iris Segmentation
(CFIS) and Gabor Analytic Iris Texture Binary Encoder (GAITBE) is proposed and
tested here. CFIS procedure is designed to guarantee that similar iris segments
will be obtained for similar eye images, despite the fact that the degree of
occlusion may vary from one image to another. Its result is a circular iris
ring (concentric with the pupil) which approximates the actual iris. GAITBE
proves better encoding of statistical independence between the iris codes
extracted from different irides using Hilbert Transform. Irides from University
of Bath Iris Database are binary encoded on two different lengths (768 / 192
bytes) and tested in both single-enrollment and multi-enrollment identification
scenarios. All cases illustrate the capacity of the newly proposed methodology
to narrow down the distribution of inter-class matching scores, and
consequently, to guarantee a steeper descent of the False Accept Rate.Comment: 8 pages, 10 figures, 11th Int. Symp. on Symbolic and Numeric
Algorithms for Scientific Computing, 200
Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features
Soft biometric information such as gender can contribute to many applications
like as identification and security. This paper explores the use of a Binary
Statistical Features (BSIF) algorithm for classifying gender from iris texture
images captured with NIR sensors. It uses the same pipeline for iris
recognition systems consisting of iris segmentation, normalisation and then
classification. Experiments show that applying BSIF is not straightforward
since it can create artificial textures causing misclassification. In order to
overcome this limitation, a new set of filters was trained from eye images and
different sized filters with padding bands were tested on a subject-disjoint
database. A Modified-BSIF (MBSIF) method was implemented. The latter achieved
better gender classification results (94.6\% and 91.33\% for the left and right
eye respectively). These results are competitive with the state of the art in
gender classification. In an additional contribution, a novel gender labelled
database was created and it will be available upon request.Comment: A pre-print version of the paper accepted at 12th IAPR International
Conference on Biometric
Face Recognition: A Novel Multi-Level Taxonomy based Survey
In a world where security issues have been gaining growing importance, face
recognition systems have attracted increasing attention in multiple application
areas, ranging from forensics and surveillance to commerce and entertainment.
To help understanding the landscape and abstraction levels relevant for face
recognition systems, face recognition taxonomies allow a deeper dissection and
comparison of the existing solutions. This paper proposes a new, more
encompassing and richer multi-level face recognition taxonomy, facilitating the
organization and categorization of available and emerging face recognition
solutions; this taxonomy may also guide researchers in the development of more
efficient face recognition solutions. The proposed multi-level taxonomy
considers levels related to the face structure, feature support and feature
extraction approach. Following the proposed taxonomy, a comprehensive survey of
representative face recognition solutions is presented. The paper concludes
with a discussion on current algorithmic and application related challenges
which may define future research directions for face recognition.Comment: This paper is a preprint of a paper submitted to IET Biometrics. If
accepted, the copy of record will be available at the IET Digital Librar
Noise Influence on the Fuzzy-Linguistic Partitioning of Iris Code Space
This paper analyses the set of iris codes stored or used in an iris
recognition system as an f-granular space. The f-granulation is given by
identifying in the iris code space the extensions of the fuzzy concepts wolves,
goats, lambs and sheep (previously introduced by Doddington as 'animals' of the
biometric menagerie) - which together form a partitioning of the iris code
space. The main question here is how objective (stable / stationary) this
partitioning is when the iris segments are subject to noisy acquisition. In
order to prove that the f-granulation of iris code space with respect to the
fuzzy concepts that define the biometric menagerie is unstable in noisy
conditions (is sensitive to noise), three types of noise (localvar, motion
blur, salt and pepper) have been alternatively added to the iris segments
extracted from University of Bath Iris Image Database. The results of 180
exhaustive (all-to-all) iris recognition tests are presented and commented
here.Comment: 5th Int. Conf. on Soft Computing and Applications (Szeged, HU), 22-24
Aug 201
Iris Recognition with a Database of Iris Images Obtained in Visible Light Using Smartphone Camera
This paper delivers a new database of iris images collected in visible light
using a mobile phone's camera and presents results of experiments involving
existing commercial and open-source iris recognition methods, namely: IriCore,
VeriEye, MIRLIN and OSIRIS. Several important observations are made.
First, we manage to show that after simple preprocessing, such images offer
good visibility of iris texture even in heavily-pigmented irides. Second, for
all four methods, the enrollment stage is not much affected by the fact that
different type of data is used as input. This translates to zero or
close-to-zero Failure To Enroll, i.e., cases when templates could not be
extracted from the samples. Third, we achieved good matching accuracy, with
correct genuine match rate exceeding 94.5% for all four methods, while
simultaneously being able to maintain zero false match rate in every case.
Correct genuine match rate of over 99.5% was achieved using one of the
commercial methods, showing that such images can be used with the existing
biometric solutions with minimum additional effort required. Finally, the
experiments revealed that incorrect image segmentation is the most prevalent
cause of recognition accuracy decrease.
To our best knowledge, this is the first database of iris images captured
using a mobile device, in which image quality exceeds this of a near-infrared
illuminated iris images, as defined in ISO/IEC 19794-6 and 29794-6 documents.
This database will be publicly available to all researchers.Comment: Accepted version of the IEEE ISBA 2016 conferenc
Comparing Haar-Hilbert and Log-Gabor Based Iris Encoders on Bath Iris Image Database
This papers introduces a new family of iris encoders which use 2-dimensional
Haar Wavelet Transform for noise attenuation, and Hilbert Transform to encode
the iris texture. In order to prove the usefulness of the newly proposed iris
encoding approach, the recognition results obtained by using these new encoders
are compared to those obtained using the classical Log- Gabor iris encoder.
Twelve tests involving single/multienrollment and conducted on Bath Iris Image
Database are presented here. One of these tests achieves an Equal Error Rate
comparable to the lowest value reported so far for this database. New Matlab
tools for iris image processing are also released together with this paper: a
second version of the Circular Fuzzy Iris Segmentator (CFIS2), a fast Log-Gabor
encoder and two Haar-Hilbert based encoders.Comment: 6 pages, 4 figures, latest version: http://fmi.spiruharet.ro/bodorin
Informatics Research Institute (IRIS) September 2008 newsletter
2007-8 was a very busy year for IRIS. It was a bumper year for visiting Profs with Prof Michael Myers visiting from New Zealand, Prof Brian Fitzgerald visiting from University of Limerick, Ireland, Prof. Uzay Kaymak visiting from Erasmus University Netherlands and Prof Steve
Sawyer visiting from Pennsylvania State University, USA. Their visits enriched our doctoral school, seminar programme workshops and our research. We were very lucky to have such a distinguished line up of visiting professors and we offer them hearty thanks and hope to keep
ongoing research links with them
Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data
Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future
DeepKey: An EEG and Gait Based Dual-Authentication System
Biometric authentication involves various technologies to identify
individuals by exploiting their unique, measurable physiological and behavioral
characteristics. However, traditional biometric authentication systems (e.g.,
face recognition, iris, retina, voice, and fingerprint) are facing an
increasing risk of being tricked by biometric tools such as anti-surveillance
masks, contact lenses, vocoder, or fingerprint films. In this paper, we design
a multimodal biometric authentication system named Deepkey, which uses both
Electroencephalography (EEG) and gait signals to better protect against such
risk. Deepkey consists of two key components: an Invalid ID Filter Model to
block unauthorized subjects and an identification model based on
attention-based Recurrent Neural Network (RNN) to identify a subject`s EEG IDs
and gait IDs in parallel. The subject can only be granted access while all the
components produce consistent evidence to match the user`s proclaimed identity.
We implement Deepkey with a live deployment in our university and conduct
extensive empirical experiments to study its technical feasibility in practice.
DeepKey achieves the False Acceptance Rate (FAR) and the False Rejection Rate
(FRR) of 0 and 1.0%, respectively. The preliminary results demonstrate that
Deepkey is feasible, show consistent superior performance compared to a set of
methods, and has the potential to be applied to the authentication deployment
in real world settings.Comment: 22 page
Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections
Two-dimensional embeddings remain the dominant approach to visualize high
dimensional data. The choice of embeddings ranges from highly non-linear ones,
which can capture complex relationships but are difficult to interpret
quantitatively, to axis-aligned projections, which are easy to interpret but
are limited to bivariate relationships. Linear project can be considered as a
compromise between complexity and interpretability, as they allow explicit axes
labels, yet provide significantly more degrees of freedom compared to
axis-aligned projections. Nevertheless, interpreting the axes directions, which
are linear combinations often with many non-trivial components, remains
difficult. To address this problem we introduce a structure aware decomposition
of (multiple) linear projections into sparse sets of axis aligned projections,
which jointly capture all information of the original linear ones. In
particular, we use tools from Dempster-Shafer theory to formally define how
relevant a given axis aligned project is to explain the neighborhood relations
displayed in some linear projection. Furthermore, we introduce a new approach
to discover a diverse set of high quality linear projections and show that in
practice the information of linear projections is often jointly encoded in
axis aligned plots. We have integrated these ideas into an interactive
visualization system that allows users to jointly browse both linear
projections and their axis aligned representatives. Using a number of case
studies we show how the resulting plots lead to more intuitive visualizations
and new insight
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