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Ordinal Feature Selection for Irisand Palmprint Recognition
Presented By:BHUWON
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Acknowledgements
Authors: Zhenan Sun, Member, IEEE,
Libin Wang, and
Tieniu Tan, Fellow, IEEE
Source: IEEE TRANSACTIONS ON IMAGE
PROCESSING, VOL. 23, NO.9, SEPTEMBER2014
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Introduction
Iris and Palmprint texture patterns are
accurate biometric modalities
Biometric features should be robust and
distinct for intra-class and inter-class
variations
Feature analysis problem can be divided into
feature representation and feature selection
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Ordinal Measures (OM) provide a good
feature representation for iris, palmprint and
face recognition
The basic idea of OM is to characterize the
qualitative image structures
Multi-lobe Ordinal Filter(MOF) is proposed to
analyze the ordinal measures of biometric
images
Introduction
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Introduction
Fig. 1 Multi-lobe Ordinal Filters
Courtesy: Zhenan Sun and Tieniu Tan
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OM are good descriptors for biometric feature
representation
There doesnt exist a generic feature set of
OM which can achieve the optimal recognition
performance for all biometric modalities
Small number of ordinal features are enough
to achieve high accuracy
Introduction
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Related Work
Feature selection is a key problem in pattern
recognition, obtaining optimal feature subset
is usually intractable
Different methods employ different
optimization functions and searching
strategies for feature selection
mRMR (Min Redundancy Max Relevance)
ReliefF
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Boosting Lasso- aims to solve least squares problem
where, gmeans the intra or inter class label,
the components of A indicate the intra or interclass matching results,
f denotes feature weight vector,and is a parameter controlling the balancebetween regression errors and sparsity ofselected features
Related Work
}||2||{||minarg 12
2 fAfgf
f
L (1)
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Model of Lassois not flexible - the
optimization does not take into account the
characteristics of image features and
biometric recognition
Both Boosting and Lasso have limitations in
ordinal feature selection
Related Work
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A feature selection method with following
properties are desired
Feature selection process can be formulated as a
simple optimization problem
Sparse solution can be achieved in feature
selection so that the selected feature set is
compact
Related Work
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Penalty of misclassification cant be a high-order
function
Model of feature selection should be flexible to
take into account the characteristics of biometricrecognition problem
Feature selection problem has less dependence
on the training data and can be solved by a small
set of training samples
Related Work
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Feature Selection based on Linear
Programming
The objective is to select a limited number of
feature units
Ordinal feature selection is formulatedas
Objective function:
(2)
D
i
ii
N
j
k
N
j
j wPNN 111min
l b d
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Subject to:
Feature Selection based on Linear
Programming
(3)
(4)
(5)
(6)
(7)
Njxw
D
i
jiji ,...,2,1,1
Nkxw
D
i
kiki ,...,2,1,1
Njj ,...,2,1,0
Nkk ,...,2,1,0
Diwi ,...,2,1,0
S l i b d i
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Dis the no. of ordinal features
N+ and N-denote the number of intra and
inter-class biometric matching pairs
wiis the weight of the ithordinal feature
Pimeasures the recognition accuracy
and are two fixed parameters indicatingthe expected intra and inter class biometric
matching results respectively
Feature Selection based on Linear
Programming
F S l i b d Li
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Basic idea of the method is to find a sparse
representation of ordinal features
Intra and inter-class biometric matching
results are expected to be well separated
No. of selected ordinal features should be
small
Feature Selection based on Linear
Programming
F S l i b d Li
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First part of the objective functionaims to
minimize the misclassification error
Second part of the objective functionenforces
weighted sparsity
Sparsity of the ordinal feature units is very
important to effective and efficient biometric
recognition
Feature Selection based on Linear
Programming
F S l i b d Li
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Eqn. 3 and Eqn. 4require that all intra and
inter class matching samples should be well
separated based on a large margin principle
Advantage of LP is that there are no. of
software tools available
CPLEX
LINDO etc.,
Feature Selection based on Linear
Programming
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Ordinal Feature Selection For Iris
Recognition
O di l F t S l ti F I i
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Large number of ordinal features are available
for iris images
Iris texture varies from region to region
Region specific ordinal filters are needed
Process of ordinal feature selection doesnt
consider the prior mask info
Ordinal Feature Selection For Iris
Recognition
O di l F t S l ti F I i
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Iris images are divided into multiple blocks
Preprocessed and normalized iris image isdivided into multiple regions
A number of di-lobe and tri-lobe ordinal filterswith variable parameters are performed to
generate ordinal feature units
Ordinal Feature Selection For Iris
Recognition
O di l F t S l ti F I i
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Ordinal Feature Selection For Iris
Recognition
Fig. 2 Ordinal Feature selection in Iris recognition
Courtesy: Zhenan Sun et al.
O di l F t S l ti F I i
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LP method is compared with Boosting, Lasso,
mRMR and ReliefF feature selection methods
CASIA-Iris database is used which contains
20000 iris images from 1000 subjects
Ordinal Feature Selection For Iris
Recognition
Ordinal Feat re Selection For Iris
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Ordinal Feature Selection For Iris
Recognition
Fig. 3 Learning result of Linear Programming
Courtesy: Zhenan Sun et al.
Ordinal Feature Selection For Iris
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Ordinal Feature Selection For Iris
Recognition
Fig. 4 Iris recognition performance as a function of no. of ordinal features
Courtesy: Zhenan Sun et al.
Ordinal Feature Selection For Iris
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Ordinal Feature Selection For Iris
Recognition
Courtesy: Zhenan Sun et al.
Ordinal Feature Selection For Iris
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Ordinal Feature Selection For Iris
Recognition
Courtesy: Zhenan Sun et al.
Fig. 5 Some typical ordinal feature units selected by LP, Lasso, Boost and mRMR. (a) LP-
OM. (b) Lasso-OM. (c) Boost-OM. (d) mRMR-OM.
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Ordinal Feature Selection for Palmprint
Recognition
Ordinal Feature Selection for Palmprint
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Ordinal Feature Selection for Palmprint
Recognition
Palmprint provides a reliable source of info
Richness of visual information available on
palmprint images provides various possibilities
for feature representation
Competitive code represents the state of the
art performance in palmprint recognition
New method for palmprint feature analysis
using ordinal measures and LP is proposed
Ordinal Feature Selection for Palmprint
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Ordinal Feature Selection for Palmprint
Recognition
Core idea is to recover the random layout of
ordinal measures for feature matching
For palmprint images, the gaps between
neighboring fingers can be used as landmark
points
For N ordinal features, the template size for
each palm print image is 128N bytes
Ordinal Feature Selection for Palmprint
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Here tri-lobe ordinal filters for palmprint
feature extraction because
Tri-lobe filters are more discriminative & robust
A much larger feature space can be generated
Di-lobe filters can be regarded as special case of
tri-lobe filters
PolyU palmprint image database is used forperformance evaluation
Ordinal Feature Selection for Palmprint
Recognition
Ordinal Feature Selection for Palmprint
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Ordinal Feature Selection for Palmprint
Recognition
Fig. 6 Illustration of generation of synthetic training dataset
Courtesy: Zhenan Sun et al.
Ordinal Feature Selection for Palmprint
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Ordinal Feature Selection for Palmprint
Recognition
Fig. 7 Illustration of di-lobe and tri-lobe ordinal filters for
palmprint image analysis. (a) Examples of di-lobe ordinal
filters. (b) Examples of tri-lobe ordinal filters
Courtesy: Zhenan Sun et al.
Ordinal Feature Selection for Palmprint
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Ordinal Feature Selection for Palmprint
Recognition
Courtesy: Zhenan Sun et al.
Method Feature size (Bytes) EER
Competitive code 384 4.21x10-4
Di-lobe OM 384 4.76x10-4
Boost-OM 256 5.55x10-4
Lasso-OM 256 6.96x10-4
LP-OM (1stround) 256 2.66x10-4
LP-OM (2ndround) 256 6.19x10-5
TABLE IICOMPARISONOFPERFORMANCEOFPALMPRINTRECOGNITIONMETHODSON
POLYU PALMPRINTIMAGEDATABASE
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Conclusion
A novel feature selection method to learn the
most effective ordinal features for iris and
palmprint recognition based on LP
LP Solution is a good solution even in
photometric and geometric variation
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References
1. T. Tan and Z. Sun, Ordinal representations for biometrics recognition, inProc. 15th Eur. Signal Process. Conf., 2007, pp. 3539.
2. Z. Sun and T. Tan, Ordinal measures for iris recognition, IEEE Trans.Pattern Anal. Mach. Intell., vol. 31, no. 12, pp. 22112226, Dec. 2009.
3. Z. Sun, T. Tan, Y. Wang, and S. Z. Li, Ordinal palmprint representation forpersonal identification, in Proc. Conf. Comput. Vis. Pattern Recognit.
(CVPR), vol. 1. 2005, pp. 279284.4. P. Viola and M. Jones, Robust real-time face detection, Int. J. Comput.
Vis., vol. 57, no. 2, pp. 137154, May 2004.
5. PolyU Palmprint Database [Online]. Available:http://www.comp.polyu.edu.hk/~biometrics/
6. S. Z. Li, R. Chu, S. Liao, and L. Zhang, Illumination invariant facerecognition using near-infrared images, IEEE Trans. Pattern Anal. Mach.Intell., vol. 29, no. 4, pp. 627639, Apr. 2007.
7. CASIA Iris Image Database [Online]. Available:http://biometrics.idealtest.org
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