=Paper=
{{Paper
|id=Vol-1353/paper_39
|storemode=property
|title=Multispectral Recognition Using Genetic and Evolutionary Feature Extraction
|pdfUrl=https://ceur-ws.org/Vol-1353/paper_39.pdf
|volume=Vol-1353
|dblpUrl=https://dblp.org/rec/conf/maics/AriasSRDA15
}}
==Multispectral Recognition Using Genetic and Evolutionary Feature Extraction==
https://ceur-ws.org/Vol-1353/paper_39.pdf
Multispectral Recognition Using
Genetic and Evolutionary Feature Extraction
Pablo A. Arias, Joseph Shelton, Kaushik Roy, Gerry V. Dozier, Foysal Ahmad
Department of Computer Science, North Carolina A&T State University, Greensboro, U.S.A
Center for Advanced Studies in Identity Sciences @ NC A&T
{parias, jashelt1, fahmad}@aggies.ncat.edu, {kroy, gvdozier}@ncat.edu
Abstract Masek 2003). Daugman developed the first iris recognition
Traditionally, iris recognition systems capture iris images in algorithm (Daugman 2004), and many researchers have
the 700 to 900nm range. It is within these ranges that further extended the work of iris recognition (Verma et al.
researchers have found the most viable iris textures for iris 2012, Bowyer, Hollingsworth and Flynn 2008, Sanchez-
recognition. Recently, there has been an interest for
Avila and Scanchez-Reillo 2005). Google headquarters
exploration of spectrum ranges that falls outside of these
traditional ranges. In this work, we will explore the uses iris recognition access control systems for identifying
performance of feature extraction techniques on a wider individuals (Adam, Neven and Steffens 2010), and the
spectrum, specifically ranges between 400nm to 1550nm. company M2SYS Technology has patented an iris
More specifically, we apply the traditional Local Binary identification system that can link hospital patients to their
Pattern (LBP) technique & a hybrid LBP technique (Genetic
medical records (Archbold 2014). In the Penobscot
and Evolutionary Feature Extraction (GEFE)) in an effort to
elicit the most important iris information. We also perform Country Jail, iris scanners are being implemented in order
intra-spectral and cross spectrum analysis on the iris images to eliminate improper inmate releases (Ricker 2006).
captured in different wavelengths. Results show that GEFE The iris biometric has advantages over other types of
outperforms the LBP technique on all spectrums. biometrics. The accuracy of recognition is generally better
than the accuracy of other biometrics such as facial or
Introduction fingerprint (Masek 2003). An iris is also well protected by
wear and tear, as opposed to one’s fingerprint. There are
Biometric technologies are becoming the predominate disadvantages in that the iris is small and intricate, which
form of access control. Physiological and behavioral traits makes it difficult to obtain from any significant distance
are aspects of biometrics that allows for unique forms of without missing vital information. Individuals who may
identification. The physiological traits (face, iris, have eye issues such as blindness or cataracts could also
fingerprint, etc.) have many advantages over the traditional difficult to recognize. Environmental issues can also be a
techniques such as knowledge and/or token based access factor in regards to poor lighting or shade. Finally, the iris
control. Knowledge can be forgotten and tokens can be can often be covered by eyewear such as glasses or shades.
easily stolen, whereas physiological traits cannot be Images captured in the near infrared (NIR) wavelength
forgotten or easily stolen. Biometrics is a subarea of the band contains more iris textural information than images
broader field of identity science. “Identity Science is the captured in the visible wavelength. Most of the current iris
field of study devoted towards the understanding of how recognition schemes capture iris images in the NIR
the dynamic nature of ‘self’ interacts with a possibly wavelength band. This range is generally from 700nm to
intractable number of dynamic environments in an effort to 900nm. Though iris recognition using NIR wavelengths
observe, track, and identify ‘self’ (in terms of its beliefs, has an acceptable recognition rate, there are still
desires, intentions, regression, progression, etc.) via a set vulnerabilities such as faking iris images (Park and Kang
of external witnesses. (Dozier 2015)”. An external witness 2005). Researchers are interested in exploring the
can be viewed as any entity that has the ability to: perceive wavelengths outside of the 700nm-900nm range.
an interaction between ‘self’ and an environment, process Most of the existing multispectral iris recognition
the observed interaction, and track and/or identify ‘self’ systems use Gabor filters as a feature extraction technique
with respect to that environment. (Daugman 2004). There has been research done using
The iris biometric has been shown to be a viable texture based feature extraction methods such as a
biometric for verification and identification (Ross 2010, Modified Local Binary Pattern (MLBP) algorithm
(Popplewell et al. 2014). This variation of the Local Binary
Copyright held by the author(s). Pattern (LBP) algorithm (Shelton et al. 2011) segments an
image into even sized regions and extracts features from segmenting an image into uniform sized, non-overlapping
each region. Each region can be referred to as a patch. regions, as shown in Figure 1. Each region has a histogram
MLBP segments a biometric image into sub-regions and associated with it, where the bins in the histogram
extracts more features from each patch than traditional correspond to the texture patterns found in each region. A
LBP. A Cooperative Game Theory (CGT) based patch FV is created by concatenating the histograms from all
selector was implemented in (Ahmad, Roy and Popplewell regions of a segmented image.
2014). Though the CGT with MLBP method showed Texture patterns are created by comparing center pixels,
promise, the approach reported in (Ahmad, Roy and a pixel that is surrounded by i number of neighboring
Popplewell 2014) utilized only the patches based on the pixels on all sides, with the i neighboring pixels. A texture
image partition of LBP. It is possible that noisy data may
pattern can be represented as a binary string, and that string
be included if patches from the entire image space are
can be decoded into a decimal value, denoted as LBP(Ni,
considered. To mitigate this, we propose the application of
c), where c is the pixel intensity value of a center pixel, N
a genetic algorithm-based feature extraction technique.
This feature extraction method evolves Feature Extractors is a set of neighboring pixel intensity values and i is the ith
(FEs) that can have patches of varying sizes in various neighboring pixel of c. LBP(Ni, c) is computed in (1) and
positions on an image and applies them in an overlapping (2), where the difference is taken between each
fashion. neighboring pixel and a center pixel. The equation s(Ni, c)
In the past, a feature extraction technique known as computes the difference and returns the appropriate bit.
Genetic and Evolutionary Feature Extraction (GEFE) was
i 1
(1)
LBP( Ni , c) s Ni , c 2i
created at the Center for Advance Studies in Identity
Science (CASIS) (Shelton et al. 2011, Shelton et al. 2014). i 0
GEFE was initially applied on facial images (Shelton et al.
0, if N i c 0
2011) but has been recently applied on iris images (Shelton s N i , c (2)
et al. 2014). 1, if N i c 0
GEFE is an instance of a Genetic and Evolutionary
Computation (GEC), an algorithm that uses natural The total number of texture patterns that can exist
selection techniques to evolve a population of candidate depend on the number of neighboring pixels, i, where the
solutions for a problem (Engelbracht 2007, Davis 1991, number of possible patterns are 2i. However, the common
Goldberg 1989). GEFE would allow sub-regions to be any way to create FVs with the LBP technique is to use mostly
location on an image and to be any size. Previous research uniform patterns for bins in the histograms. A uniform
has shown success of GEFE when testing on a single pattern is one where the bit transitions in a texture pattern
modality; GEFE outperformed traditional LBP in terms of changes two or fewer times when traversing the texture
recognition accuracy as well as the number of features pattern circularly.
used. In this work, we will apply GEFE towards The common variation of the LBP technique is popular,
multispectral iris recognition (O’Connor et al. 2014). More but it is also possible to simply consider all of the possible
specifically, we will conduct inter-spectral and cross- patterns as opposed to just uniform patterns. In this case,
spectral recognition on a wide range of spectrums (450nm- histograms would be length 2i, or, for a neighborhood size
1550nm) to determine the effectiveness of GEFE on this of 8, 28 = 256.
dataset. During the process of recognition, a probe template, p, is
The remainder of this paper is as follows. In Section 2, compared to a gallery set of vectors G ={g0, g1, ..., gk-1}
we will discuss the feature extraction methods used. In using the (Manhattan) City Block distance metric. This
Section 3, we will describe the experimental setup. Section distance is a numerical representation of the distinction
4 will contain the results of the experiments and Section 5 between two biometric instances and can be calculated
provides the conclusions and future remarks. using the following formula:
Feature Extraction Methods 𝑑 = ∑𝑛𝑖=0 |𝑝𝑖 − 𝑔𝑘,𝑖 | (3)
Local Binary Patterns where d is the distance between two subjects, p is the probe
The Local Binary Pattern (LBP) feature extraction feature template, g is the gallery feature template in set G,
algorithm is a method that is used for texture n is total number of features, i is the index of the feature,
classification(Ojala, Pietikainen and Maenpaa 2002, and k is the kth individual in the gallery. The subject, gk, is
Ahonen, Hadid and Pietikainen 2006). This technique can considered a match to p when distance between the two
be used to classify textures patterns in images and it uses vectors is the smallest compared to all other subjects in G.
these textures to create Feature Vectors (FVs) for images.
For biometric recognition, the LBP technique works by
In the original implementation of GEFE, a set of FEs
were evolved on a training set to produce FEs that could
correctly identify subjects in that particular data set. To
produce FEs that could generalize well on unseen subjects,
supervised learning was added to the GEFE process. Cross
validation in Genetic and Evolutionary Feature Extraction
– Machine Learning (GEFEML) (Shelton et al. 2012) is
done by initially generating a population of random FEs.
Every candidate FE is then evaluated on the training set
and additionally evaluated on a validation set. The results
of the FEs on the validation set do not affect the training of
FEs. While a stopping condition has not been met, FEs are
selected to breed, and offspring FEs are created. The
offspring are evaluated on the training set, but they are also
applied on the validation set. The FE with the best results
Figure 1: Image partitioned into patches
on the validation set is stored as FE*. FE* is only updated
when a new candidate FE performs better on the validation
GEFEML set than the currently stored FE*. The offspring are used to
GEFE is an instance of a GEC that evolves LBP-based FEs. create the new population and this process repeats until the
Whereas a traditional LBP FE uses even sized, non- stopping condition has been met. Under this design, FE*
overlapping patches over an entire image, GEFE evolves should generalize better on unseen subjects opposed to the
FEs that can have patches of varying sizes in various best performing FE on the training set.
positions on an image. Because GEFE is an instance of a
GEC, a FE must be represented as a candidate solution. We Experiments
use a 6-tuple with 5 sets and 1 single value, represented as
We conducted our experiments on a multispectral iris
. Each of the patches in a particular FE,
fei, are designed using the values in the 6-tuple. The Xi and image dataset that contains 38,129 images (Multispectral
Yi sets hold the points of the center of each patch in Iris Dataset). These images were acquired using
fei, while the sets Wi and Hi holds the width and heights of Goodrich/Sensors with a custom designed lens package to
the patches. The set Mi denotes a masking value for each acquire iris images at wavelengths in the range 400 to
patch in fei. Though there can be multiple patches defined 1600nm. We segmented the multispectral iris images using
by the 6-tuple, a patch’s specific masking value determines Canny edge detections in an effort to identify the iris
whether the features extracted by that patch are included in regions and applied circular Hough transforms to define
the resulting FV. the iris and pupil boundaries (Masek 2003, Popplewell et
The fitness, fi, is determined by applying fei towards a al. 2014). A technique based on Daugman’s Rubber Sheet
dataset of subject’s iris images. A subject has a number of Model was then used for normalization. These images
images that vary, and these images are separated into a were divided into 13 sections, each section depicting a
probe set and a gallery set (G). The fei is applied on these spectral band consisting of roughly 2945 iris images. For
images to create FVs, and the FVs in the probe set are our first experiment, we did intra-class comparisons for
compared to all of the FVs in the gallery set using the each spectral band. We divided the data set into three
Manhattan distance measure. The two FVs that have the sections: training, validation, and testing. The training set
least Manhattan distance are considered to be matches. If a had a total of 44 subjects, the validation set had 18
probe FV is incorrectly matched with a gallery FV, then fei subjects, and the testing set had 29 subjects. We further
is said to cause an error.. The resulting fi is the number of separated each set into a probe set and a gallery set; the
errors (𝜀) added to the percent of patches not masked out first sample of each subject went into the probe set while
(ζ), shown below. the remaining went into the gallery set. For GEFE, we ran
it for 30 runs and for each run, we ran it for 1000
𝑓_𝑖 = 10𝜀 + ζ (4) generations. For cross spectral analysis, we used features
extractors evolved from intra-spectral comparisons and
Previous research in (Shelton et al. 2011) has shown that applied them to each of the differing spectral band images.
GEFE instances with uniform patch sizes had a statistically In this work, similarity scores are computed by modifying
better performance than GEFE instances with non-uniform the Manhattan city block distance metric. The variables hi
patches. This means that the sets Wi and Hi will have one and hj represents two FVs being measured, l represents the
value in their set, representing the parameters for all length of the FV, and z represents the current position in
patches in fei. the FV.
LBP and found that 7 columns by 10 rows was the best
performing partition. This 70 patch LBP partition is
compared to GEFE in the results. Figures 2-5 show the Results show that for all spectrums, GEFE outperforms
normalized iris regions with the ovarlapped patches. traditional LBP within their respective spectrums ‘GEFE
’ and ‘GEFE ’ had similar performances in
respect to recognition accuracy, but with respect to the
Results and Discussions number of patches, GEFE was proven to be
Shown in Table I are the test set accuracies of FE produced statistically better. The results show that on average, the
by different feature extraction algorithms. In the table, 800 nm spectrum performs the best for identification
Spectrum represents the spectrum used for feature accuracy for both intra-class comparisons and cross
extraction on the training and validation set. For the Cross spectrum comparisons. An ANOVA test was used as a
Spectrum, this represents the spectrum used for the test set. statistical measure of performance, with a 95% confidence
Accuracy represents the identification accuracy of the interval.
algorithm on the test set. For LBP, the number represents In Figures 2-5, the images show the best performing FEs
the accuracy of the traditional LBP feature extractor that on the 800nm test set. In Figures 2 and 3, the best opt-gen
partitions an image into 7 by 10. For the GEFE variations and val-gen FEs are shown. The areas with overlapping
on the iris, GEFE represents FEs that were patches are the most salient areas to extract features from
optimized on the training set, while GEFE for identification. Figures 4 and 5 show the best cross-
represents the best performing FEs on the validation set. spectral FEs on the 800nm test set for opt-gen and val-gen.
For GEFE, the number on the outside represents the It appears that the right most area of iris images contains
accuracy of the best feature extractor, whereas the number more salient texture information than the left area.
within the parenthesis represents the average accuracy of Depending on if an iris is a left or right eye; there will be
the 30 best FEs. The column P represents the number of slight noisy data in the form of eyelashes. It could be the
patches used on average by the feature extractors. case that the grouping of patches on the right side is
locking on to that noisy data or lack of. In Figures 6-9, we
Table I. Performance on multispectral iris dataset. show the Cumulative Match Characteristic (CMC) curves
Accuracy and the Receiver Operator Characteristic (ROC) curves for
Patches
Cross LBP and the best FE from GEFE. The CMC curve plots
Spectrum
Spectrum GEFE the rank accuracies of the methods, while the ROC curve
LBP Opt Val
plots the True Accept Rate (TAR) and the False Accept
405 7.02 13.79(8.05) 12.07(6.21) 28.16 22.87 Rate (FAR) of subjects. The results in Table 1 show that
405
800 n/a 96.55(89.83) 94.83(84.02) 28.16 22.87 the 800 nm spectrum achieved rank 1 accuracy of 98.28%
505 45.61 63.79(53.79) 53.44(41.03) 32.80 24.15 for both the opt-gen and val-gen FEs on the test set.
505
800 n/a 94.83(92.59) 96.55(86.09) 32.80 24.15
620 64.91 79.31(74.02) 74.13(60.80) 33.71 24.25
620
1200 n/a 98.28(92.01) 93.10(81.49) 33.71 24.25
700 50.87 67.24(59.31) 63.79(56.84) 30.96 24.48
700
800 n/a 96.55(93.62) 94.83(8.91) 30.96 24.48
800 91.23 98.28(94.54) 98.28(87.07) 28.76 24.71
800
911 n/a 98.28(91.38) 94.83(84.26) 28.76 24.71
910 89.66 96.55(92.18) 96.55(84.37) 31.48 25.08 Figure 2: 800nm FE(Opt Gen) on 800nm Image.
910
800 n/a 96.55(87.30) 96.55(87.30) 25.08 25.08
911 87.93 96.55(90.75) 93.10(81.72) 31.22 24.88
911
800 n/a 98.27(94.60) 96.55(86.32) 31.22 24.88
970 84.48 89.66(85.17) 87.93(75.46) 30.83 24.12
970
800 n/a 98.28(94.60) 94.83(85.46) 30.83 24.12
1070 89.66 94.83(89.31) 93.10(77.24) 30.92 25.08
1070 Figure 3: 800nm FE (Val Gen) on 800nm Image.
800 n/a 98.28(95.11) 96.55(87.99) 30.92 25.08
1200 93.10 98.27(92.36) 93.10(78.79) 31.62 24.78
1200
800 n/a 98.28(94.66) 94.83(85.75) 31.62 24.78
1300 82.76 93.10(89.66) 91.38(77.99) 33.59 25.27
1300
800 n/a 96.55(93.39) 94.83(85.00) 33.59 25.27
1450 36.21 67.24(58.16) 51.72(31.72) 38.55 25.08
1450
911 n/a 98.28(92.76) 64.83(80.11) 38.55 25.08 Figure 4: 1070nm FE (Opt Gen) on 800nm Image.
1550 31.03 43.10(36.94) 37.93(24.60) 34.15 24.71
1550
800 n/a 96.55(92.01) 96.55(86.49) 34.15 24.71
Figure 5: 1070nm FE (Val Gen) on 800nm Image.
The CMC curves plot the accuracy at each rank. The rank
represents the ranking of match scores for all probe
subjects. For Figure 6 and 7, the ROC curve plots the rate
of impostor attempts accepted on the x-axis, against the
corresponding rate of genuine attempts accepted on the y-
axis along an increasing threshold. In Figure 8, the CMC
curves show a superior performance of GEFE compared to
LBP. Though both techniques do not achieve 100%
accuracy until rank 57, GEFE continues to outperform
LBP. In Figure 9, the CMC curves for cross spectrum Figure 7: ROC Curves for Cross-Spectral
analysis were created by taking the best performing FEs Matching
from each spectrum when used for cross validation. It
appears that the FE from the 800nm spectrum performed
best in cross spectrum analysis. This supports the intra-
class results, where the FEs on the 800nm spectrum had
the best performance overall.
Even though the results show that FEs evolved on the
800 nm spectrum had the overall best performance, it is
worth mentioning that some bands outside of the NIR
range performed well. In Table 1, the performance of
GEFE on the 620nm spectrum performed better than the
700nm spectrum not only in intra-class matching, but also
using feature extractors from the 1200 nm spectrum. The
performance of the 1200 nm feature extractors performed
similarly to the ranges of 910nm - 970nm. The proposed
work using GEFE achieved 61% TAR at 1% FAR, while
the FEs on the 910, 911, and 970 nm spectral bands
achieved 48%, 60%, and 62% TAR respectively at 1%
FAR. Figure 8: CMC Curves for Intra-Class Matching
Figure 9: CMC Curves for Cross Spectral Matching.
Figure 6: ROC Curves for Intra-Spectral Matching
Conclusion and Future Work Masek, L. 2003. Recognition of human iris patterns for biometric
identification. Dissertation B.Sc. thesis, University of Western
We find from the experimental results that GEFE Australia.
outperforms the LBP approach for cross spectral and intra- Multispectral Iris Dataset: Portions of the research in this paper
spectral analysis. The best performing wavelength for the use the Consolidated Multispectral Iris Dataset of iris images
collected under the Consolidated Multispectral Iris Dataset
entire dataset was the 800nm wavelength for recognition Program, sponsored by the US Government.
accuracy. However, there seems to be promise with feature
O’Connor, B., Roy, K., Shelton J., and Dozier, G. 2014. Iris
extractors evolved on the 1200 nm wavelength images. Recognition using fuzzy level set and GEFE. International
Future work will be focused on fusing the cross spectral Journal of Machine Learning and Computing (IJMLC), vol. 4, no.
data in order to evolve features across several different 3, pp. 225 – 231.
spectral bands. This may improve accuracy by extracting Ojala, T., Pietikainen, M., and Maenpaa, T. 2002. Multiresolution
features that may not have been present within a single gray-scale and rotation invariant texture classification with local
spectrum. binary patterns. IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 24, no. 7, pp. 971-98.
Park, J. H., and Kang, M. G. 2005. Iris recognition against
Acknowledgements counterfeit attack using gradient based fusion of multi-spectral
images. Advances in Biometric Person Authentication, vol. 3781,
This research was funded by the Army Research pp. 150-156.
Laboratory (ARL) for the multi-university, Center for Popplewell, K., Roy, K., Ahmad, F., and Shelton, J. 2014.
Advanced Studies in Identity Sciences (CASIS) and by the Multispectral Iris Recognition Utilizing Hough Transform and
National Science Foundation (NSF), Science & Modified LBP. Proc. in IEEE Conference on Systems, Man and
Technology Center: Bio/Computational Evolution in Cybernetics (SMC), pp. 1396-1399.
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