Ordinal Feature Selection for Iris and Palmprint Recognition (2)

8/10/2019 Ordinal Feature Selection for Iris and Palmprint Recognition (2)

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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:


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


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


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


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.,


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


Recognition



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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


Recognition



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Recognition

Fig. 2 Ordinal Feature selection in Iris recognition

Courtesy: Zhenan Sun et al.



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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


Recognition

Ordinal Feat re Selection For Iris


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Recognition

Fig. 3 Learning result of Linear Programming




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Recognition

Fig. 4 Iris recognition performance as a function of no. of ordinal features




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Recognition




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Recognition


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



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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



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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



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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


Recognition



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Recognition

Fig. 6 Illustration of generation of synthetic training dataset




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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




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Recognition


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
http://www.comp.polyu.edu.hk/~biometrics/http://www.comp.polyu.edu.hk/~biometrics/http://www.comp.polyu.edu.hk/~biometrics/http://biometrics.idealtest.org/http://biometrics.idealtest.org/http://biometrics.idealtest.org/http://biometrics.idealtest.org/http://www.comp.polyu.edu.hk/~biometrics/http://www.comp.polyu.edu.hk/~biometrics/

Documents

Ordinal Feature Selection for Iris and Palmprint Recognition (2)