BIO-DISTANCE - Revision history

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Created page with "{{ResearchProjInfo |Title=BIO-DISTANCE |ContactInformation=Fernando Alonso-Fernandez |ShortDescription=Biometrics at a Distance |Description=Despite the practical importance a..."

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{{ResearchProjInfo
|Title=BIO-DISTANCE
|ContactInformation=Fernando Alonso-Fernandez
|ShortDescription=Biometrics at a Distance
|Description=Despite the practical importance and the advantages of biometric solutions to the task of verifying
personal identity, their adoption has proved to be slower that predicted. Biometrics “on the move” is
a hottest research topic aimed to acquire biometric data “at a distance” as a person walks by detection
equipment. This drastically reduces the need of user’s cooperation, achieving low intrusiveness and
thus, high acceptance and transparency.

With these ideas in mind, the main objective of this project is to investigate a number of activities
aimed to make biometric technologies applicable to data acquired at a distance and/or on the move. We
propose the use of face and iris as the reference modalities, being the two traits that are attracting
more efforts thanks to the possibility of their simultaneous acquisition. This project is an integrated
approach that covers the whole structure of a biometric system, including basic research and
algorithm development for the different stages of the system, as well as practical results through case
studies implementation and evaluation. To accomplish the overall objectives, a number of challenges
need to be overcome, which constitute the specific research objectives.
|LogotypeFile=Grid.jpg
|ProjectResponsible=Fernando Alonso-Fernandez
|ProjectStart=2011/01/07
|ProjectEnd=2013/06/30
|ApplicationArea=Biometrics
}}
__NOTOC__
{{ShowResearchProject}}

FP7-PEOPLE-2009-IEF Marie Curie Action
Project No: 254261

[[Image:MC_FP7actions.jpg|150px]]

== Quality Measures ==

Several quality measures of the iris region have been implemented and evaluated, both locally (iris
boundaries) and globally (whole eye region), including the proposal of novel algorithms. We have
aimed to quantify image properties reported in the literature as having the greatest influence in iris
recognition accuracy, in support of the standard ISO/IEC 29794-6 Biometric Sample Quality (part 6:
Iris image). The algorithms implemented include measurements of: defocus blur (1 algorithm),
motion blur and image interlace (1 algorithm), contrast of iris boundaries (2 algorithms), circularity
of iris boundaries (1 algorithm), gray scale spread (2 algorithms), and occlusion (1 algorithm).

The algorithms are described in publication [2] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below]. The following is a slide from the conference presentation:

[[Image:irisqualitymeasures.jpg|600px]]

== Detection and segmentation algorithms ==

=== Iris segmentation algorithm ===

A complete system that allows the segmentation of iris boundaries and the detection of occluding
eyelids has been developed. It is based on the Generalized Structure Tensor (GST) theory, introduced
20 years ago by Prof. [[Josef Bigun]] to analyze local orientation of images. Using manual
segmentation as benchmark for our experiments, our system has shown a considerable improvement
over popular methods (such as the Circular Hough Transform or the Integro-Differential operator)
that do not analyze local orientations of the image. The results obtained show the validity of our
proposed approach and demonstrate that the GST constitutes an alternative to classic iris
segmentation approaches.

The GST segmentation algorithm is described in publications [5] and [6] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below]. The following are some slides from the conference presentations:

[[Image:GSTsegmentation.jpg|600px]]

[[Image:GSTsegmentationeyelids.jpg|600px]]

[[Image:GSTsegmentationresults.jpg|600px]]

[[Image:GSTsegmentationresultseyelids.jpg|600px]]

=== Impact of quality components in the iris segmentation ===

We have also analyzed the impact of image defocus and motion blur in the segmentation. Reported
results shows the superior resilience of the GST compared with the popular segmentation methods
indicated, with similar performance in pupil detection (inner iris boundary), and clearly better
performance in sclera detection (outer iris boundary) for all levels of degradation. It is worth noting
here than the outer iris boundary (sclera) is usually more difficult to detect than the inner iris
boundary (pupil). Since the pupil is generally darker than the iris, there will be a sharper transition
between the pupil and the iris, which is easier to detect than the softer transition between the iris and
the sclera. Therefore, our GST system contributes with a better detection of sclera boundaries than
traditional systems.

This quality study is described in publication [5] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below].

The impact of the eight quality components of above in the segmentation performance has also been studied.
Quality measures are computed locally (around the iris boundaries) and
some of them are also computed globally (in the whole image of the eye region). It has been found
that local quality metrics are better predictors of the segmentation accuracy than global metrics,
despite the obvious limitation of requiring segmentation. Some measures also behave differently
when they are computed locally or globally.

This quality study is described in publication [2] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below].

=== Detection of eye regions ===

Accurate segmentation of iris boundaries demands the use of good quality images acquired in more
or less controlled conditions. With the purpose of going towards a less-controlled and distant
acquisition, we have developed a system for detection of the complete eye region. The target is to
make detection of human faces possible over a wide range of distances, even when the iris texture
cannot be reliably obtained (low resolution, off-angle, etc.) or under partial face occlusion (close
distances, viewing angle, etc.). This is a necessary step, for example, to zoom into the iris texture and
segment the iris boundaries, or to allow tracking of human faces when the whole face is not
available. Existing face detection algorithms model faces in a holistic manner, so they fail in case of
face occlusion or sometimes simply with arbitrary (non-frontal) poses. On the other hand, the iris
texture can be very difficult to obtain in distant acquisition. The task proposed is an intermediate
step, i.e. detection of the eye region, not relying on having neither the full face available nor a
well-defined iris texture. The approach for this purpose has been the use of symmetry filters and the
Generalized Structure Tensor theory (as in the segmentation of iris boundaries above, where we have
used circular filters only). We have started with the use of symmetry filters capable of detecting
patterns of concentric circles. Our assumption is that the region around the eye center shows this kind
of pattern, and even considering that the iris circles are not exactly concentric, this approach has
provided promising results. This kind of filters has been evaluated with images acquired with a
webcam from a portable tablet PC, showing strong detection capabilities of eye regions.

The following is a slide showing some detection results on eye images from the [http://paginas.fe.up.pt/~mobBIO2013/ MobBIO database]:

[[Image:detectioneyeregions.jpg|600px]]

== Feature extraction and matching algorithms ==

=== Impact of quality components in the matching stage ===

We have also evaluated the impact of quality components (using the measures developed above) in
the performance of two iris matchers based on Log-Gabor wavelets and SIFT keypoints (the first one
is a popular, publicly available iris matcher, while the second one is based on public SIFT source code
which has been adapted to work on iris images). We observe that the matchers are also sensitive to quality variations,
but not necessarily in the same way than the segmentation algorithm. Also, the SIFT matcher is
observed to be more resilient to segmentation inaccuracies. In this sense, errors in the segmentation
may be hidden by the matcher, pointing out the importance of evaluating also the precision of iris
segmentation, as proposed in this project, rather than focusing on recognition accuracy only.

This quality study is described in publication [2] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below].

It is relevant to note that the success of iris segmentation is crucial for the good performance of iris
recognition systems. Previous research works treat iris recognition systems as a black box, evaluating
the impact of quality factors at the output of the system (i.e. by looking at the recognition
performance). The same procedure is traditionally followed to evaluate new developments in the
segmentation stage. This project has brought innovative aspects in the sense that it has focused on
evaluating directly the precision of the segmentation. The observed results, with segmentator and
matcher not necessary behaving in the same way, support the proposed evaluation framework.
Experiments also show that quality measures are not necessarily correlated. Quality is intrinsically
multi-dimensional and it is affected by factors of very different nature. Research done in this
direction includes fusing the estimated quality measures to obtain a single measure with higher
prediction capability of the segmentation and matching accuracy. On-going research also includes
exploiting the different sensitivity observed in the two matchers, so by using adaptive quality fusion
schemes, we are seeking to obtain better performance over a wide range of qualities.

=== Personal recognition based on periocular information ===

Periocular recognition is a direction that has gained attention recently in biometrics. Periocular refers
to the face region in the vicinity of the eye, including the eye, eyelids, lashes and eyebrows. This
region can be easily obtained with existing setups for face and iris, and the requirement of user
cooperation can be relaxed. An evident advantage of the periocular region is its availability over a
wide range of acquisition distances even when the iris texture cannot be reliably obtained (low
resolution, off-angle, etc.) or under partial face occlusion (close distances). Most face recognition
systems use a holistic approach, requiring a full face image, so the performance is affected in case of
occlusion. In addition, current commercial iris systems have constrained image acquisition
conditions, prompting the user to position the eye in front of the sensor (normally at ~ 20-40 cm). In
the current context of ubiquitous access to information, with the proliferation of portable devices and
mobility requirements, the relaxation of acquisition constraints is a factor that have great (if not the
greatest) impact in mass acceptance levels of these technologies. Other application fields that are
receiving much attention are forensics and surveillance, where the requirement of controlled
acquisition is simply impossible. These considerations of a more flexible and less intrusive
acquisition are central for the objectives of this project, and therefore periocular work is of high
relevance.

Work in this direction has consisted in the development of a periocular recognition system based on
retinotopic sampling grids positioned in the pupil center, followed by Gabor decomposition at
different frequencies and orientations. Our system achieves competitive verification rates compared
with existing recognition approaches, based mainly on Local Binary Patterns and Gradient
Orientation histograms. We have also carried out experiments which demonstrate the validity of our
recognition system in less-controlled images. For example, it does not need an accurate detection of
the iris region, but it is enough with having the center of the eye. This makes this system useful in a
wide range of applications where the iris texture cannot be obtained, and provides the way to
integrate it with the detection system of eye regions developed in the previous sub-objective.

The periocular recognition system is described in publications [3] and [4] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below]. The following are slides from the conference presentations and the poster presenting publication [4]:

[[Image:periocular_recognition.jpg|600px]]

[[Image:periocularresults1.jpg|600px]]

[[Image:periocularresults2.jpg|600px]]

[[Image:2012_ECCV_poster_FAlonso.jpg|600px]]

== Multibiometric fusion ==

The periocular system has been also compared with a dedicated iris expert based on a popular public
software which uses 1D log-Gabor filters. The dedicated iris expert works much better than the
proposed periocular approach, something that encourages us to continue the improvement of our
system. The fusion of iris and periocular systems also shows a relative improvement in some of our
experiments. This points out the potential complementarity of the iris texture and the periocular
region, as reported in some other works in the literature. Fusion between the systems is done using
adaptive logistic regression, which has shown superior performance than other fusion rules in
previous research work of the fellow.

The fusion is evaluated periocular recognition system is described in publication [4] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below].

Existing face recognition systems are based on a global model of the face, thus failing if the whole
face is not available. We are also working in the fusion of periocular information with other local
face regions, where the identity model of the person is built from the available regions. This is the
approach being followed in the literature to cope with incomplete faces.

== ICIR2013, the First ICB Competition on Iris Recognition ==

We have participated with developments of this project in ICIR2013, the First ICB Competition on Iris Recognition.
This competition has been organized by the Institute of Automation, Chinese Academy of Sciences (CASIA), in conjunction with the 6th
International Conference on Biometrics (ICB-2013). The objective was to evaluate iris recognition
algorithms on data including motion blur, non-linear deformation, eyeglasses and specular
reflections. We participated with an iris segmentation
stage based on the GST and an iris matching stage based on the fusion of retinotopic sampling grids
and SIFT keypoints. The submission obtained excellent results, ranked in second position out of 13
participating systems.

Our submission to ICIR2013 is briefly described in the report [1] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below]. The following is a slide with an snapshot from the website of the competition.

[[Image:ICIR2013.jpg|600px]]

== References ==

# F. Alonso-Fernandez, J. Bigun, “Halmstad University submission to the First ICB Competition on Iris Recognition (ICIR2013)”, Technical Report, Halmstad University, August 2013 ([http://hh.diva-portal.org/smash/record.jsf?searchId=2&pid=diva2:643009 link])
# F. Alonso-Fernandez, J. Bigun, “Quality Factors Affecting Iris Segmentation and Matching”, Proc. [http://atvs.ii.uam.es/icb2013 6th IAPR Intl Conf on Biometrics, ICB,] Madrid, June 4-7, 2013 ([http://hh.diva-portal.org/smash/record.jsf?searchId=2&pid=diva2:607425 link])
# F. Alonso-Fernandez, J. Bigun, “Biometric Recognition Using Periocular Images”, Proc. [http://www.medtechwest.se/events/ssba-2013-2/ Swedish Symposium on Image Analysis, SSBA] (Goteborg, Sweden), March 14-15 2013 ([http://hh.diva-portal.org/smash/record.jsf?searchId=2&pid=diva2:607423 link])
# F. Alonso-Fernandez, J. Bigun, “Periocular Recognition Using Retinotopic Sampling and Gabor Decomposition”,Proc. [https://sites.google.com/site/wiaf2012/ Intl Workshop “What's in a Face?” WIAF,] in conjunction with the [http://eccv2012.unifi.it/ European Conference on Computer Vision, ECCV,] Springer LNCS-7584, pp. 309-318, Firenze, Italy, October 7-13, 2012 ([http://hh.diva-portal.org/smash/record.jsf?searchId=2&pid=diva2:545741 link])
# F. Alonso-Fernandez, J. Bigun, “Iris Boundaries Segmentation Using the Generalized Structure Tensor. A Study on the Effects on Image Degradation”, Proc. [https://sites.google.com/a/nd.edu/btas_2012/ Intl Conf on Biometrics: Theory, Apps and Systems, BTAS,] Washington DC, September 23-26, 2012 ([http://hh.diva-portal.org/smash/record.jsf?searchId=2&pid=diva2:545745 link])
# F. Alonso-Fernandez, J. Bigun, “Iris Segmentation Using the Generalized Structure Tensor”, Proc. [http://www.kth.se/en/csc/forskning/cvap/2.33720/welcome-1.85593 Swedish Symposium on Image Analysis, SSBA] (Stockholm, Sweden), March 7-9 2012 ([http://hh.diva-portal.org/smash/record.jsf?searchId=2&pid=diva2:549739 link])

@@ Line 30: / Line 30: @@
 of iris boundaries (1 algorithm), gray scale spread (2 algorithms), and occlusion (1 algorithm).
-The algorithms are described in publication [2] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below]. The following is a slide from the conference presentation:
+The algorithms are described in publication [2] [http://wiki.hh.se/caisr/index.php/BIO-DISTANCE#References below]. The following is a slide from the conference presentation:
 [[Image:irisqualitymeasures.jpg|600px]]
@@ Line 48: / Line 48: @@
 segmentation approaches.
-The GST segmentation algorithm is described in publications [5] and [6] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below]. The following are some slides from the conference presentations:
+The GST segmentation algorithm is described in publications [5] and [6] [http://wiki.hh.se/caisr/index.php/BIO-DISTANCE#References below]. The following are some slides from the conference presentations:
 [[Image:GSTsegmentation.jpg|600px]]
@@ Line 70: / Line 70: @@
 traditional systems.
-This quality study is described in publication [5] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below].
+This quality study is described in publication [5] [http://wiki.hh.se/caisr/index.php/BIO-DISTANCE#References below].
 The impact of the eight quality components of above in the segmentation performance has also been studied.
@@ Line 79: / Line 79: @@
 when they are computed locally or globally.
-This quality study is described in publication [2] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below].
+This quality study is described in publication [2] [http://wiki.hh.se/caisr/index.php/BIO-DISTANCE#References below].
 === Detection of eye regions ===
@@ Line 120: / Line 120: @@
 segmentation, as proposed in this project, rather than focusing on recognition accuracy only.
-This quality study is described in publication [2] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below].
+This quality study is described in publication [2] [http://wiki.hh.se/caisr/index.php/BIO-DISTANCE#References below].
 It is relevant to note that the success of iris segmentation is crucial for the good performance of iris
@@ Line 165: / Line 165: @@
 integrate it with the detection system of eye regions developed in the previous sub-objective.
-The periocular recognition system is described in publications [3] and [4] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below]. The following are slides from the conference presentations and the poster presenting publication [4]:
+The periocular recognition system is described in publications [3] and [4] [http://wiki.hh.se/caisr/index.php/BIO-DISTANCE#References below]. The following are slides from the conference presentations and the poster presenting publication [4]:
 [[Image:periocular_recognition.jpg|600px]]
@@ Line 186: / Line 186: @@
 previous research work of the fellow.
-The fusion is evaluated  periocular recognition system is described in publication [4] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below].
+The fusion is evaluated  periocular recognition system is described in publication [4] [http://wiki.hh.se/caisr/index.php/BIO-DISTANCE#References below].
 Existing face recognition systems are based on a global model of the face, thus failing if the whole
@@ Line 204: / Line 204: @@
 participating systems.
-Our submission to ICIR2013 is briefly described in the report [1] [http://islab.hh.se/mediawiki/index.php/BIO-DISTANCE#References below]. The following is a slide with an snapshot from the website of the competition.
+Our submission to ICIR2013 is briefly described in the report [1] [http://wiki.hh.se/caisr/index.php/BIO-DISTANCE#References below]. The following is a slide with a snapshot from the website of the competition.
 [[Image:ICIR2013.jpg|600px]]

@@ Line 2: / Line 2: @@
 |Title=BIO-DISTANCE
 |ContactInformation=Fernando Alonso-Fernandez
-|ShortDescription=Biometrics at a Distance
+|ShortDescription='''Biometrics at a Distance'''
 |Description=Despite the practical importance and the advantages of biometric solutions to the task of verifying personal identity, their adoption has proved to be slower that predicted. Biometrics ''on the move'' is a hottest research topic aimed to acquire biometric data ''at a distance'' as a person walks by detection equipment. This drastically reduces the need of user’s cooperation, achieving low intrusiveness and thus, high acceptance and transparency.

@@ Line 1: / Line 1: @@
 {{ResearchProjInfo
-|Title=BIO-DISTANCE (Biometrics at a Distance)
+|Title=BIO-DISTANCE
 |ContactInformation=Fernando Alonso-Fernandez
 |ShortDescription=Biometrics at a Distance
-|Description=Despite the practical importance and the advantages of biometric solutions to the task of verifying personal identity, their adoption has proved to be slower that predicted. Biometrics “on the move” is a hottest research topic aimed to acquire biometric data “at a distance” as a person walks by detection equipment. This drastically reduces the need of user’s cooperation, achieving low intrusiveness and thus, high acceptance and transparency.
+|Description=Despite the practical importance and the advantages of biometric solutions to the task of verifying personal identity, their adoption has proved to be slower that predicted. Biometrics on the move is a hottest research topic aimed to acquire biometric data at a distance as a person walks by detection equipment. This drastically reduces the need of user’s cooperation, achieving low intrusiveness and thus, high acceptance and transparency.
 With these ideas in mind, the main objective of this project is to investigate a number of activities aimed to make biometric technologies applicable to data acquired at a distance and/or on the move. We propose the use of face and iris as the reference modalities, being the two traits that are attracting more efforts thanks to the possibility of their simultaneous acquisition. This project is an integrated approach that covers the whole structure of a biometric system, including basic research and algorithm development for the different stages of the system, as well as practical results through case studies implementation and evaluation. To accomplish the overall objectives, a number of challenges need to be overcome, which constitute the specific research objectives.

@@ Line 45: / Line 45: @@
 [[Image:irisqualitymeasures.jpg|600px]]
-== Detection and segmentation algorithms ==
+== Iris Detection and Segmentation Algorithms ==
@@ Line 186: / Line 186: @@
 [[Image:2012_ECCV_poster_FAlonso.jpg|600px]]
-== Multibiometric fusion ==
+== Multibiometric Fusion ==
 The periocular system has been also compared with a dedicated iris expert based on a popular public

@@ Line 31: / Line 31: @@
 [[Image:MC_FP7actions.jpg|150px]]
-== Quality Measures ==
+== Iris Quality Measures ==
 Several quality measures of the iris region have been implemented and evaluated, both locally (iris
@@ Line 117: / Line 117: @@
 [[Image:detectioneyeregions.jpg|600px]]
-== Feature extraction and matching algorithms ==
+== Iris Feature Extraction and Matching Algorithms ==

@@ Line 224: / Line 224: @@
 # F. Alonso-Fernandez, J. Bigun, “Quality Factors Affecting Iris Segmentation and Matching”, Proc. [http://atvs.ii.uam.es/icb2013 6th IAPR Intl Conf on Biometrics, ICB,] Madrid, June 4-7, 2013 ([http://hh.diva-portal.org/smash/record.jsf?searchId=2&pid=diva2:607425 link])
 # F. Alonso-Fernandez, J. Bigun, “Biometric Recognition Using Periocular Images”, Proc. [http://www.medtechwest.se/events/ssba-2013-2/ Swedish Symposium on Image Analysis, SSBA] (Goteborg, Sweden), March 14-15 2013 ([http://hh.diva-portal.org/smash/record.jsf?searchId=2&pid=diva2:607423 link])
-# F. Alonso-Fernandez, J. Bigun, “Periocular Recognition Using Retinotopic Sampling and Gabor Decomposition”,Proc. [https://sites.google.com/site/wiaf2012/ Intl Workshop “What's in a Face?” WIAF,] in conjunction with the [http://eccv2012.unifi.it/ European Conference on Computer Vision, ECCV,] Springer LNCS-7584, pp. 309-318, Firenze, Italy, October 7-13, 2012 ([http://hh.diva-portal.org/smash/record.jsf?searchId=2&pid=diva2:545741 link])
+# F. Alonso-Fernandez, J. Bigun, “Periocular Recognition Using Retinotopic Sampling and Gabor Decomposition”, Proc. [https://sites.google.com/site/wiaf2012/ Intl Workshop “What's in a Face?” WIAF,] in conjunction with the [http://eccv2012.unifi.it/ European Conference on Computer Vision, ECCV,] Springer LNCS-7584, pp. 309-318, Firenze, Italy, October 7-13, 2012 ([http://hh.diva-portal.org/smash/record.jsf?searchId=2&pid=diva2:545741 link])
 # F. Alonso-Fernandez, J. Bigun, “Iris Boundaries Segmentation Using the Generalized Structure Tensor. A Study on the Effects on Image Degradation”, Proc. [https://sites.google.com/a/nd.edu/btas_2012/ Intl Conf on Biometrics: Theory, Apps and Systems, BTAS,] Washington DC, September 23-26, 2012 ([http://hh.diva-portal.org/smash/record.jsf?searchId=2&pid=diva2:545745 link])
 # F. Alonso-Fernandez, J. Bigun, “Iris Segmentation Using the Generalized Structure Tensor”, Proc. [http://www.kth.se/en/csc/forskning/cvap/2.33720/welcome-1.85593 Swedish Symposium on Image Analysis, SSBA] (Stockholm, Sweden), March 7-9 2012 ([http://hh.diva-portal.org/smash/record.jsf?searchId=2&pid=diva2:549739 link])