Download - finger prints
R & D PROPOSAL
RELIABLE FINGERPRINT MATCHING
SUBMITTED BY
A.ROHINI
B.LALITHA DEVI
P.K.SHEELA SHANTHA KUMARI
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ABSTRACT
• One of the most difficult problem in human identification is fingerprint .
• Fingerprint Matching is significantly influenced by fingertip surface condition,which may very depending on enviromental or personal causes.
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Cont..
• Minutae based matching has difficulty in quickly matching of two fingerprint images.
• I proposed filter based algorithm and Eucledian distance algorithm using to show an accuracy of fingerprint matching..
• Finally I showed that the Reliable Fingerprint Matching is achieved by this project.
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Biometric Technique
• Biometric is the science of verifying & establishing the identity of an individual through physiological features or behavioral traits.
Physiological biometric
Fingerprint, Hand geometry,face,Iris.
Dependent on environmental/Interactions.
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Fingerprint Classification
• Fingerprint is an impression of ridges on the skin.it can be classified in to 5 categories.
arch tented arch right loop Left loop
whorl
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EXISTING SYSTEM
• The Existing system uniaueness of the ridges flow pattern is the basis of forensic application on fingerprints,they taken small images of fingerprint and gave solution to the valid minutae points.
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PROPOSED SYSTEM
• Fingerprint verification using Enhanced Modified Direction feature and Neural based classification with fingerprint images.
• I proposed
How we extract the minutiae points, &
How we establish whether two ` impression belong to the same finger.
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contd..
• By using gabor filters size of the image has calculated.
• By using Eucledian distance algorithm I search the position of ridges and to which identify the nearest number of ridges has taken to calcuate distance between the ridges.
• Knowing the distance after that I calculated Ridges and pore configuration.
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Features of Fingerprint Matching
• Shape - Lot of variations in the shape of
minutiae.• Ridges - Orientation is defined in the
ridge area. Configuration-Measurement accuracy
can be seen by comparing.
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SYSTEM ARCHITECTURE
USER INTERFACE
ENROLLMENT
MINUTAEEXTRACTOR
QUALITY CHECKER
AUTHENTICATION
MINUTAEEXTRACTOR
MINUTAE MATCHER
DATA BASE
VALID/FAKE
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Cont..
• Four components in architecture
User interface
System Database
Enrollment
Authentication
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cont…
• The uniqueness of a configuration or ridges depends on several factors such as whorl ,loop, arch involved in the respective of shape and sizes.
• These factors matched with databse and give authentication to user,But sometimes not matched in live scan process,
• In my project any type of fingerprint condition is there,it is sequentially matched and give authentication to user.
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MODULES
• SOURCE MODULE
• EXTRACTOR MODULE
• MATCHING MODULE
• ACCURACY MODULE
• AUTHENTICATION MODULE
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SOURCE MODULE
• Read Image file,The client progam is responsible for this to provide image for testing .
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EXTRACTOR MODULE
• Fingerprint enhancement is essential to ensure the robustness of fingerprint identification with respect to the image quality.
• Gabor filter is introduced in this paper
• Gabor filtering is the most popular method in fingerprint enhancement
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Cont...
• The enhancement performance is assessed on standard fingerprint databases. Experimental results show that the proposed Gabor filtering method can effectively improve the fingerprint image quality and promote the reliability of fingerprint identification.
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ALGORITHM
• Pattern or Image based algo:Step 1: Pattern based algorithms compare the basic fingerprint patterns (arch, whorl, and loop) between a previously stored template and a candidate fingerprint.
Step 2: This requires that the images be aligned in the same orientation.
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Cont…
• Step3: The algorithm finds a central point in the fingerprint image .
• Step4: In a pattern-based algorithm, the template contains the type, size,
and orientation of patterns within the aligned fingerprint image.
• Step 5: The candidate fingerprint image is graphically compared with the template to determine the degree to which they match
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MATCHING MODULE
• MINUTAE CONFIGURATION:• When Matching is based on minutae,the
template consist of vital information about these ffeatures.
• {Rp,Ro, Rt….. Rp (ME),Ro (ME), Rt (ME)}Rp- Ridge positionRo-Ridge orientationRt -Type of ridgeME – Minutae enrolled
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Cont..
• The position gives relative to the local reference point size & shape could be stored.
Configuration = R*(1/No)^Nr
R -type of Ridge
No- No,of different orientation
Nr-No of ridges in the configuration
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PORE CONFIGURATION
• Neighboring pores are separated by constant distance ‘d’.
• D-distance between neighboring pores.
• Value of distance d is calculate by using this formula
d-(area of the ridge/No .of .pores)
confgtn- distance* N p* 0.48
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ALGORITHM –K NN algo
The k-nearest neighbors algorithm (k-NN) is a method for classifying objects based on closest training examples in the feature space.
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ACCURACY MODULE
• A Matching score provides the the degree of matching between two segments with a range of complete match.
Matching score = (Minutae config value + pore config value ) * 100
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AUTHENTICATION MODULE
• A Fingerprint is scanned and is compared to the template in a database ,if they match valid finger print, If not matched it is Fake.
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USER Read image
Save image as identifier
Smoothing
Filtering
Scaling
Database
Verification
Remove background images
Filtering fake minutae
Scaling the length of the image
DATA FLOW DIAGRAM
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TESTING
• Analyzed 20 various fingerprint image average result
LIVE SCAN MATCHING SCORES
55% 54% 63%
ALGORITHM USING MATCHING SCORE 97% 98% 99%
RING INDEX THUMB
Above 95-High accuracy (using algorithm )
50 % matching is image matched with database( ordinary live scan process)
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Experimental results
Proposed Enhancement of
algorithm gives good results compared with the existing system
Experimental results shows that the performance and efficiency are improved with
my implementation.0
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INDEX RING THUMB
LIVE SCAN SENSOR
ALGORITHM USINGWITH LIVE SCANSENSOR
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CONCLUSION
• This project demonstrates an efficient fingerprint recognition using minutiae matching, the proposed technique is particularly effective for verifying quality fingerprint images, Every feature was required to match for the entire set to match
• Proposed enhancement of algorithm for detection of minutiae give good results in reducing the false minutiae improvements, due to low quality level can also refer to the unification of image filtering and segmentation algorithm and minutiae detection, In order to make the entire process faster
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The endThe end