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Transcript of 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