ppt fingerprint

7/28/2019 Ppt Fingerprint

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Fingerprint Based Ignition

System

ARIJIT MITRA

SUNNY DUTTA

SUMIT KUMAR SHRIVASTAVA

NOOR HASSAN

Mentor: Prof Siladitya Sen


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INDEX

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Biometric information is widely used these days in various security systems.Fingerprints are the oldest and perhaps most widely used for biometricidentifications

Here we have chosen to focus on the use of fingerprints for vehicle ignition asopposed to the use of conventional keys which are not that safe in this ageof modern technology

Block Diagram:

Introduction


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System Blocks

1.Fingerprint Recognition Software

2.Hardware interface module

3.Ignition System Module

4


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Fingerprint

A fingerprint is the feature pattern of one finger . Each person has his

own fingerprints with the permanent uniqueness. So fingerprints have being

used for identification and forensic investigation for a long time

Fingerprint


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Fingerprint Classification

Left Loop Right Loop

Whorl Arch Tented Arch

DeltaPore


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Minutia

However, shown by intensive research on fingerprint

recognition, fingerprints are not distinguished by their

ridges and furrows, but by Minutia

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Minutiae-Based Approach

Minutiae

terminations bifurcations

Ridge Valley


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Verification (AFAS) vs. Identification (AFIS)

Sensor

Minutia

Extractor

Minutiae

Matcher

System

Database

System Level Design

SystemDatabase

Users

Magnetic

Card.

User

1:m Match

Identification

1:1 MatchVerification

User ID


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Algorithm Level Design

Thinning

Minutiae Marking

Remove False Minutiae

Minutia extraction

Preprocessing

Image Segmentation

Image EnhancementImage Binarization

Post-processing

Minutia Extractor:


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Algorithm Level Design

Find Reference Minutia Pair

Affined TransformReturn Match Score

Minutia Matcher:


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Minutia Extractor- Segmentation

Block directional estimation

Foreground : have a dominant directionBackground : No global direction


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Fingerprint Image Segmentation

Ridge Flow Orientation Estimate

Edge detector: get gradient x (gx),gradient y (gy)

Estimate the according to:tg2 = 2 sigma(gx*gy)/sigma(gx

2-gy2)

Region of Interest

Morphological Method

Close + Open


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Area Close Open

ROI + Bound


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Fingerprint Image Enhancement

Histogram Equalization


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Fingerprint Image Enhancement

Fourier Transform

i


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Preprocessing - Enhancement


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Fingerprint Image Binarization


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Common Approaches:

Local Adaptationgray value of each pixel g

if g > Mean(block gray value) , set g = 1;

Otherwise g = 0

Directly ridge Retrieval from Gray Image

get Ridge Maximums Implying binarization



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Directly ridge Retrieval

1.Estimate ridge direction D2.Advance by a step length3.Along the direction orthogonal to D

Return to ridge Center4.go to 1

1.Block ridge flow orientation O2.Get direction P orthogonal to O3.Project block image to the lines along P


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Minutia extraction stage - Thinning


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Minutia extraction stage - Thinning

Morphological Approaches:

bwmorph(binaryImage,''thin'',Inf)

Parallel thinning algorithm:

1) 2=< N(p1)


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Preprocessing Steps:0 1 0

0 1 01 0 1

0 0 00 1 0

0 0 1

Bifurcation

Termination

Minutia extraction


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Minutia extraction


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Post-processing stage

False Minutia Remove:

Two disconnected terminations

short distance

Same/opposite direction flow

Two terminations at a ridge

are too close


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Post-processing stage

False Minutia Remove:


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Minutia Match

Minutia Representation:

Mn ( Position, Direction , Associate Ridge)

tg = (yp-y0)/(xp-x0);

Xp = sigma(xi)/Lpath;Yp = sigma(yi)/Lpath;

ridge

Minutiax0 x1 x2 x3 x4 x5 x6 x

y

Lpath

Generally, ridge endings and bifurcations are consolidated


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Simple Relax Match Algorithm :

Minutia Match

1. For each pair of Minutia

2. Construct the Transform Matrix

TM =

cos

sin

0

sin

cos

0

0

0

1

x

y

xi_new

yi_new

i_new

xi x( )

yi y( )

i

=TM *

(x,y, )

(xi,yi, i)


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Simple Relax Match Algorithm :

Minutia Match

For any two minutia from different image,If They are in a box with small length

And their direction has large consistence

They are Matched Minutia

Match Score =

Num(Matched Minutia)

Max(Num Of Minutia (image1,image2));


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Alignment based Algorithm :

Minutia Match

ridge

Minutiax0 x1 x2 x3 x4 x5 x6 x

y

Ridge information is used to

determine the goodness of areference Minutia pair

Ridge_direction

If two ridge are matched well

Continue use the

Relax Box Match Or

Use

String Match


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Fingerprint Verification

Performance Evaluation Index

FRR: False Rejection Rate

FRR = 2/total1

FAR: False Acceptance Rate

FAR = 3/total2

Total1 = m*(n+1)*n/2

Total2 = m*(m-1)/2

SameFinger

Program

result (Yes/No)

Different

Finger

1 Yes 2 No

3 Yes 4 No

F10 F11 F12 F13 F1n

F20 F21 F22 F23 F2n

F30 F31 F32 F33 F3n

Fm0 Fm1 Fm2 Fm3 Fmn


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APPLICATIONS

FINGER PRINT WALLET

MOBILE


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DRAWBACKS

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