a fingerprint bio-cryptosystem to mitigate fingerprint uncertainity in
TRANSCRIPT
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A FINGERPRINT BIO-CRYPTOSYSTEM TO
MITIGATE FINGERPRINT UNCERTAINITY IN
MODIFIED VORONOI NEIGHBOR
STRUCTURES
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INTRODUCTION
Machine learning: deals with the construction and study ofsystems that can learn from data, rather than follow onlyexplicitly programmed instructions
Pattern recognition:
The assignment of a physical object orevent to one of several pre-specified categories
A patternis an object, process or event that can be given aname.
A pattern class(or category) is a set of patterns sharingcommon attributes and usually originating from the samesource.
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CONT
During recognition(or classification) given objects are assigned to
prescribed classes
A classifieris a machine which performs classification
Examples of application
Biometrics
Military Applications
Diagnostic systems
Optical character recognition
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CONT
BIO-CRYPTOGRAPHY
Bio-cryptography combines cryptography with bio-metrics
Bio-cryptosystems provide security
i)securing the cryptographic key using biometric features
ii)Generating the cryptographic key directly from biometrics
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OBJECTIVE
To design an alignment-free bio-cryptosystem based on modified
voronoi neighbor structures
To provide local structural stability and distortion insensitivity.
The proposed system is able to provide good security and high
recognition rate.
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RELATED WORKS
K.Nandakumar,A.K.Jain,S.Pankanti,Fingerprint based fuzzy
vault: implementation and performance,IEEE transactions on
Information Forensics and security,Pg no:744-757,Vol:2,2007
Problem: To protect the fingerprint template of a user typicallystored in a database or a smart card
Solution: Fuzzy vault scheme protects both secret key and biometric
template
Disadvantage: Alignment of query fingerprint with template is a
challenging task
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W.Yang,J Hu,S.Wang,A Delaunay Triangle-Based Fuzzy
Extractor for Fingerprint Authentication, Proceedings of the
IEEE 11thInternational conference on trust, security and Privacy in
Computing and Communications,Pg no:66-70,2012
New scheme: Registration-free Delaunay triangle based fuzzy
extractor
Fuzzy extractor consists of two primitives:
1.Secure sketch
2.Fuzzy Extractor
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K.Xi,J.Hu,F.Han,An alignment free fingerprint fuzzy extractor
using near equivalent dual layer structure check algorithm,
Proceedings of the IEEE Conference on Industrial Electronics and
applications,Pp:1040-1045,2011
Problem: Feature alignment, Verification Accuracy, Information
Accuracy
Solution: Minutia local structure features: Dual layer structure check
New fuzzy extractor scheme based on NeDLSC
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Wencheng Yang et al,Analignment free bio-cryptosystem based
on modified voronoi neighbor structures,ELSEVIER,1309-
1320,Vol no:47,2013
Problems: Fingerprint uncertainty caused by distortion ,rotation andpre-alignment
Solution:Voronoi neighbor structures provide local structural
stability and distortion insensitivity
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SYSTEMMODEL
Preprocessing &
minutiae
extraction
Preprocessing
&minutiae
extractionPreprocessing &
minutiae
extraction
Formation of VNSs
Generation of modified
VNSs
Generation of fixed-length bit-
string representations
Encrypted Matching
Template
fingerprint
Query
Fingerprint
Match/Non-
match
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Orientation fieldestimation Fingerprint regionlocalizationSmoothing filter
Minutia
ExtractionThinning Ridge Detection
Fig: Flowchart of Pre-processing and Minutia Extraction
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Minutia
Extraction
Modified
VNSs3D array
Template
features
Secure
Sketch
Sketched
data set
Secure
Sketch(SS)
B_VNST
i
SS(B_VNS )T
i
Secret
Key
Hash
Key Binding
Polynomial
Fig :The Encoding Stage
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Minutia
Extraction
ModifiedVNSs
3D array Queryfeatures
Sketched
data set
Recover RecoveredFeature set
Secret Key
Recover
module
SuccessRec(B_VNSQj ,SB_VNSTi )
FailNext decoding attempt
B_VNSQ
j
SB_VNST
i
Key
Retrieving
Polynomial
reconstruction
Fig: The decoding Stage13
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TOOLS
Language: Java
JDK 8
Netbeans 8-IDE
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