phase2 FINAL

A Presentation on

 “SIMULATION OF PALM PRINT IDENTIFICATION BASED ON ZERNIKE

MOMENT”

Internal guide:

Mr. MITUL M. PATELAsst. Prof, E&C Dept.PIET, Limda

Prepared By:SHARMA ASHOK S.Enrollment No.100370722003

Master of EngineeringDigital Communication

2012-13

AGENDA

• Introduction• Palm Print• Literature Review• Palm Print Extraction• Preprocessing• Feature Extraction• Matching• Conclusion• References

INTRODUCTION• BIOMETRICS:

– Biometrics identification is the technique of automatically identifying or verifying an individual by physical characteristics or personal trait.

– Types:• Behavioral• Physiological

CLASSIFICATION OF BIOMETRICS

BIOMETRIC CHARACTERISTICS

• Universality

• Permanence

• Uniqueness

• Collectability

• Acceptability [13] Palmprint Recognition Based On 2-d Gabor Filters - Baris Konuk

DESIRED FEATURES IN A BIOMETRIC

• High Accuracy• Permanence of biometric in time• Utilization of cheap acquisition devices• Resistance to changes in environmental conditions• No or very little public objection (Acceptability)• Small template size• Simple user – system interaction

[13] Palmprint Recognition Based On 2-d Gabor Filters - Baris Konuk

ADVANTAGES OF PALMPRINT

• Palm print has relatively stable and unique features

• Collection of data is easy and non-intrusive

• Devices to collect data are economical

• It provides high efficiency using low resolution images

[11] An Efficient Occlusion Invariant Palmprint Based Verification System -Naresh kumar kachhi

LITERATURE REVIEW

• Li Fang, Maylor K.H. Leung, Tejas Shikhare, Victor Chan, Kean Fatt Choon, “Palmprint Classification” 2006 IEEE International Conference on Systems, Man, and Cybernetics, October 8-11, 2006– Classification of palm print in different categories.

• Madsu Hanmandlu, Neha Mittal “A comprehensive study of palmprint based authentication”, International Journal of Computer Applications, Jan 2012– Use of new features for palm print recognition.

LITERATURE REVIEW• K. B. Nagasundara, D. S. Guru “Multi-algorithm based

Palmprint Indexing”, International conference & workshop on Recent Trends in Technology, 2012– Proposed approach based on the fusion of Haar wavelets and

Zernike moments.

• Amir Tahmasbhi, Fatemeh Saki, Shahriar B. Shokouhi “Classification of benign and malignant masses based on Zernike moments” Elsevier, Computers in Biology and Medicine, 2011– Development of a novel Computer-aided Diagnosis (CADx) using

Zernike Moments.

LITERATURE REVIEW• Atif Bin Mansoor , Hassan Masood, Mustafa Mumtaz ,

Shoab A. Khan “A feature level multimodal approach for palm print identification using directional sub band energies” Journal of Network and Computer Applications, AUGUST 2010– Different technique for ROI extraction.

JUSTIFICATION OF TOPIC

Need to build system which is robust to translation and rotation, has constraint free acquisition, and uses low cost scanner.

APPROACH

1. ROI extraction

2. Preprocessing

3. Feature extraction

4. Feature matching

5. Decision

BLOCK DIAGRAM

[9] Palmprint verification with moments –Ying Han Pang, Andrew T.B.J

DATABASE

Poly U database Sr. No.

Simulation parameters

Values

1 Capturing device CCD

2 Spatial resolution 75 dpi

3 Gray levels 256

4 No. of images used

200

5 Images per palm 10

6 Images for training

140

7 Images for testing 60

Image from Poly U Palmprint database

[10] http://www.commp.polyu.edu.hk/~biometrics

DATABASE

Acquiring the image for database

[13] Palmprint Recognition Based On 2-d Gabor Filters - Baris Konuk

PRINCIPLE LINES

[13] Palmprint Recognition Based On 2-d Gabor Filters - Baris Konuk

RESOLUTION REQUIREMENTS FOR DIFFERENT PALM PRINT FEATURES

Palm Print Features Required Resolution (in dpi)

Principal Lines ≥75

Wrinkles ≥100

Ridges texture ≥125

[13] Palmprint Recognition Based On 2-d Gabor Filters - Baris Konuk

RESULTSAND

ANALYSIS

ROI(Region of Interest)

1. CAPTURE IMAGE

2. BINARIZATION3. CONTOUR4. DISTANCE

TRANSFORM5. SELECT

REFERENCE POINT

6. SELECTING ROI

7. CROPPING ROI

. c(x,y)

PREPROCESSING

HISTOGRAM EQUILIZATION

ROI

Histogram

FEATURE EXTRACTION

• Zernike moments are used as features.• Provides good discrimination ability.• The order of Zernike moments determines the

details of information regarding palm print.• Higher the order of moments, greater the details of

the image. • But higher orders are sensitive to noise.

[15] Image Analysis by Moments –Simon Xinmeng Liao

ZERNIKE MOMENTS

• Mapping of an image onto a set of complex Zernike polynomials.

• Orthogonal to each other.

• Represent the properties of an image with no redundancy or overlap of information between the moments.

[15] Image Analysis by Moments –Simon Xinmeng Liao

ZERNIKE MOMENT:

p-|q|=even and |p|≤qwherewhere

[14] Moments and Moment Invariants in Pattern Recognition-Jan Flusser, Tomáš Suk and Barbara Zitová

TRAINNING THE SYSTEM

ROI Z00

1 0.020686

2 0.019612

3 0.019556

: :

: :

TRAINNING THE SYSTEMROI Z11

1 -0.0023663+.021545i

2 0.0035283-0.008104i

3 0.0046994-0.01299i

: :

: :

TRAINNING THE SYSTEMROI Z20 Z22

1 0.0026986 -0.015113-0.0043729i

2 -0.017006 -0.015396-0.0097199i

3 -0.026876 -0.0070583-0.006422i

: : :

: : :

MATCHING

Test image

Train images

EUCLIDEAN DISTANCE

• If p = (p1 ,p2,...,pn) and q = (q1 ,q2,...,qn) are two points, then the distance from p to q is given by

MATCHING

Capture image

Extract ROI

Extract features

MATCHINGNo Z00 Z11 Z20 Z22

1 0.003483

0.00456+.001

55i

-0.0063

21

0.005645-

0.0131i

2 0.003215

0.005646-

0.05464i

-0.0005

123

0.00046213-0.006i

3 0.006321

0.003483-.01546i

-0.0005

123

0.003215+0.0

054i

.

.

.

MATCHING

Test image

Database

RESULTS

For order0,1 and 2, using a test image:

ROI with index 27

dmin = 0.0037 - 0.0075i

Test image

MINIMUM DISTANCE

MINIMUM DISTANCE

MINIMUM DISTANCE

INDEX MATCHING

PERFORMANCE EVALUATION• FALSE ACCEPTANCE RATE:• FALSE REJECTION RATE:• FRR =

• Receiver Operator Curve (ROC)

• Efficiency =

[9] Palmprint verification with moments –Ying Han Pang, Andrew T.B.J

SAMPLE ROC CURVES

[13] Palmprint Recognition Based On 2-d Gabor Filters - Baris Konuk

FAR-FRR GRAPH FOR THRESHOLD

[9] Palmprint verification with moments –Ying Han Pang, Andrew T.B.J

THE ROC CURVE

FAR-FRR GRAPH TO OBTAIN THRESHOLD VALUE

DISTANCE HISTOGRAM

LAGENDRE MOMENT:

Where p+q is the order, p,q=0,1,2…∞

[14] Moments and Moment Invariants in Pattern Recognition-Jan Flusser, Tomáš Suk and Barbara Zitová

COMPARISION

Order of moments Efficiency

Zernike Legendre

0,1 68.3333 30.0000

0,1,2,3 81.3559 80.0000

0,1,2,3,4,5 77.7777 66.6666

0,1,2,3,4,5,6,7 86.0465 33.3333

0,1,2,3,4,5,6,7,8,9 68.5714 10.0000

CONCLUSION

• From this work it is concluded that there is a need of Better biometric system for person authentication with lower resolution capturing device.

• By designing biometric system using palm print we solved the above issue.

• Use of Zernike moments for identification makes it robust to rotational and translational changes.

REFERENCES

[1] Vivek Kanhangad, Ajay Kumar, David Zhang, “A Unified Framework for Contactless Hand Verification”, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL.6, NO.3, SEPTEMBER 2011

[2] Atif Bin Mansoor , Hassan Masood, Mustafa Mumtaz , Shoab A. Khan “A feature level multimodal approach for palm print identification using directional sub band energies” Journal of Network and Computer Applications, AUGUST 2010

[3] Li Fang, Maylor K.H. Leung, Tejas Shikhare, Victor Chan, Kean Fatt Choon, “Palmprint Classification” 2006 IEEE International Conference on Systems, Man, and Cybernetics, October 8-11, 2006

[4] K. B. Nagasundara, D. S. Guru “Multi-algorithm based Palmprint Indexing”, International conference & workshop on Recent Trends in Technology, 2012

[5] Jian-GangWanga,Wei-Yun Yaua, Andy Suwandya, Eric Sungb “Person recognition by fusing palm print and palm vein images based on “Laplacian palm” representation”, 17 October 2007

[6] Zhu Le-qing, Zhang San-yuan, “Multimodal biometric identification system based on finger geometry, knuckle print and palm print”, 1 June 2010

[7] Zhenhua Guo, Wangmeng Zuo, Lei Zhang, David Zhang, “A unified distance measurement for orientation coding in palm print verification ”, 4 September 2009

[8] Madsu Hanmandlu, Neha Mittal “A comprehensive study of palmprint based authentication”, International Journal of Computer Applications, Jan 2012

[9] Ying Han Pang, Andrew T.B.J “Palmprint verification with moments” Journal of WSCG, Vol.12, No.1-3, ISSN 1213-6972

[10] The Poly U palmprint database.http://www.commp.polyu.edu.hk/~biometrics

[11] Naresh kumar kachhi, “An Efficient Occlusion Invariant Palmprint Based Verification System”, June 2009

[12] Diogo Santos Martins, “Biometric recognition based on the texture along palmprint principal lines”,July 2011

[13] Baris Konuk, “Palmprint Recognition Based On 2-d Gabor Filters” Jan 2007

[14] Moments and Moment Invariants in Pattern Recognition-Jan Flusser, Tomáš Suk and Barbara Zitová

[15] Image Analysis by Moments –Simon Xinmeng Liao

THANK YOU