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finger prints finger prints Presentation Transcript

    • R & D PROPOSAL
    • RELIABLE FINGERPRINT MATCHING
            • SUBMITTED BY
            • A.ROHINI
            • B.LALITHA DEVI
            • P.K.SHEELA SHANTHA KUMARI
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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.
  • SYSTEM ARCHITECTURE USER INTERFACE ENROLLMENT MINUTAE EXTRACTOR QUALITY CHECKER AUTHENTICATION MINUTAE EXTRACTOR MINUTAE MATCHER DATA BASE VALID/ FAKE
  • Cont..
    • Four components in architecture
    • User interface
    • System Database
    • Enrollment
    • Authentication
  • 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.
  • MODULES
    • SOURCE MODULE
    • EXTRACTOR MODULE
    • MATCHING MODULE
    • ACCURACY MODULE
    • AUTHENTICATION MODULE
  • SOURCE MODULE
    • Read Image file,The client progam is responsible for this to provide image for testing .
  • 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
  • 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.
  • 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 .
  • 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
  • MATCHING MODULE
    • MINUTAE CONFIGURATION:
    • When Matching is based on minutae,the template consist of vital information about these ffeatures.
    • {R p ,R o , R t ….. R p (ME) ,R o (ME ) , R t (ME) }
    • R p- Ridge position
    • Ro-Ridge orientation
    • Rt -Type of ridge
    • ME – Minutae enrolled
  • Cont..
    • The position gives relative to the local reference point size & shape could be stored.
            • Configuration = R*(1/N o )^Nr
            • R -type of Ridge
            • No- No,of different orientation
            • Nr-No of ridges in the configuration
  • 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
  • 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.
  • 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
    • 2
  • 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.
  • DATA FLOW DIAGRAM FAKE VALID USER Read image Save image as identifier Smoothing Filtering Scaling Database Verification Remove background images Filtering fake minutae Scaling the length of the image
  • TESTING
    • Analyzed 20 various fingerprint image average result
    RING INDEX THUMB Above 95-High accuracy (using algorithm ) 50 % matching is image matched with database( ordinary live scan process) LIVE SCAN MATCHING SCORES 55% 54% 63% ALGORITHM USING MATCHING SCORE 97% 98% 99%
  • 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 .
  • 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
  • The end