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Icetet 2010 id 94 fkp segmentation
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Transcript of Icetet 2010 id 94 fkp segmentation
Dr. H B Kekre, Mr. V A Bharadi
Department of Computer Science, NMIMS University, MPSTME,
Ville Parle (West), Mumbai-56
[email protected], [email protected]
Paper ID # 94
ICETET 2010
1. Introduction
2.Finger-knuckle Print
3.Region of Interest
4. Gradient & Orientation Calculation
5. Local & Angular Difference
7. Results
6. Coordinate System
8. Conclusion
Biometrics
Physical Characteristics – Fingerprints, Palmprint, Face, Iris etc.
Behavioral Characteristics – Signature, Speech, Gait.
Finger-Knuckle Print is an emerging biometric trait.
Figure 1. Finger-Knuckle Print
Acquisition Device
Figure2. Typical Finge-Knuckle-Print Image from Hong
Kong Polytechnic University FKP Database[7].
Acquisition
• Biometric Trait is acquired
• Converted to Standard Format
Pre-processing
• Noise Removal
• ROI Segmentation
Feature Extraction
• Extract Feature Vector
• Store Feature vectors for training
Matching
• Matching Feature Vector for classification
Multistep process based on Gradient & Sum of Cosine of
orientation field.
Gives flexible selection of ROI.
Fast enough to implement in real time systems.
Based on the fact that the FKP images are rich in
texture. This texture information is used for orientation
firld calculation.
Orientation filed is used for Localizing the Phalangeal
joint which is the center of co-ordinate system of ROI.
Orientation Field & Coherence of FKP
Image (a) Field overlayed on FKP image
(b) Actual Plot of gradient orientation field
(c) Coherence of FKP Image (Block size is
16X16Pixels)
Testing Summary
Total Images
Tested
Successfu
lFailure
Average testing time
per Image
502 483 19 110 milliseconds
Proposed technique is tested on the Hong
Kong Polytechnic University Finger-
Knuckle-Print database [7], this database
comes with the ROI images, and we have
compared our results with the given ROI
images. The program is written in Microsoft
Visual C# 2005, tested on AMD Athlon
64FX, 1.8GHz Processor, Windows XP
SP3 Operating System (32 Bit).
In this paper we have proposed a new technique to
segment the region of interest of Finger-Knuckle-Print
images.
This technique can be used in the preprocessing step for
implementing FKP verification.
The technique is fast and takes average 110 ms to
segment the ROI.
We have used Gradient Orientation and field strength to
detect the center of ROI, the accuracy given by
proposed technique is 96.21%, this method is another
viable practical approach to real time FKP ROI
segmentation.
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7. http://www.comp.polyu.edu.hk/~biometrics/FKP.htm
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Biometrics Research Center, Department of Computing, The Hong Kong Poly. University
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Recognition", Biometrics Research Center, Department of Computing, The Hong Kong Polytechnic
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Forensics and Security 4(1),98–109,(2009)
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1–6 (2007)
16. H B Kekre, V A Bharadi, "Fingerprint Core Point Detection Algorithm Using Orientation Field Based
Multiple Features", International Journal of Computer Applications (0975 - 8887), Volume 1 – No. 15, pp.
106-112
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Field”, Applied Mathematics and Computation 185(2007) 823-833, Science Direct, Elsevier.