PRESENTED BY:- H.O.D:- GUIDED BY :- 1. Miss. AMRITA MITRA Prof. N.J.JANWE Prof. MANISHA MORE 2. Mr.GIRIPRASAD Hungund Dept. of Comp. Tech Dept. of Comp. Tech 3. Mr. RAHUL DHAKATE R.C.E.R.T R.C.E.R.T 4. Mr. ASHIT VERMA PROJECT INCHARGE

Prof.MANISHA PISEDept. of Comp. TechR.C.E.R.T

Classification of Biometric Systems

Biometric Features

voice

Mimic

Handwriting Keyboard Behavior

Active Features Retina

Odor

FingerprintHand

GeometryVein

structure

Face

Ear

Iris

Passive Features

Universality.Uniqueness.High permanence. Cost effective.Most mature and proven technique.

Fingerprint AnatomyFingerprint is pattern of ridges and valleys on the surface of fingerUniqueness determined by overall pattern of ridges and valley and local ridge anomalies like ridge bifurcation or ridge ending (minutiae points)

Ridges

Valleys

Minutiae

Ridges, Valleys and automatically detectedminutiae points in a fingerprint image

(local level features)

Minutiae features

Measurement of Minutiae

Local Features Global Features

1998: Hong proposed the use of Gabor filter for an enhancement.

2000: Anil k. Jain proposed filterbank based matching technique.

2002: Garis, Jiang and Yau proposed binarisation method for fingerprint.

2003: Sahil Prabhakar and sharat Pankati proposed advancement in filterbank method.

2005: Dinesh Kapoor and Himanshu Bhatnagar combined filterbank with minutiae to developed new approach called hybrid approach.

Verification /Authentication modeIdentification/Recognition mode

ObjectiveTo produce a fingerprint verification system that can verify print sample with the pre-established database.

SYSTEM DESIGN:

Steps in feature extraction1.Determine a reference point and region of interest for the fingerprint image.2.Tessellate the region of interest around the reference point.3.Filter the region of interest in eight different direction using a bank of Gabor filters.4.Compute the average absolute deviation from the mean (AAD) of gray values in individual sectors in filtered images to define the FingerCode (feature vector)

15

Reference Point is point of maximum curvature of concave ridges in

fingerprint images.

Example Results

Steps in determining the Reference Point Location

1. Division the input image into non-overlapping block of size w x w.2. Computation of the gradients ∂x(i,j) and ∂y(i,j) at each pixel (i,j).3. Estimation the Local ridge orientation of each block centered at pixel (I , j) by the following equations:

Reference Point Location (contd.)4. Smoothen the Orientation field to convert the image into continuous vector field.

With resulting vector field do low pass filtering with W filter.

Reference Point Location (Contd.) The smoothed orientation field at (i , j) is computed as follows

5. Compute E, image containing only sine component of smoothed orientation field.

6. Regions RI and RII are determined by applying the reference point location algorithm over a large database to capture the maximum curvature in concave ridges.

Reference Point Location (Contd.)

7. Integrate sine component of the orientation field in regions RI and RII .

8.Find the maximum value in A and assign its co-ordinates to the reference point.

9. Repeat steps 1-8 for fixed number of times & restrict the search for the reference point in the localneighborhood of the detected reference point

Example Results

The region of interest is divided into collection of all the sectors Si, where

b is width of each band, k is number of sectors considered in each band and i=0…..(B x k-1) where B is number of concentric bands considered around the reference point for feature extraction.

Parameters depend upon the image resolution and size.

Reference point (x), the region of interest, and 80 sectors superimposed on a fingerprint.

Reference Point

Sectors

Stage-1: Normalization. For all the pixels in sector Si, the normalizedimage is defined as,

•I(x,y) :Gray level at pixel (x,y).• Mi and Vi :Estimated mean and variance of sector Si.• Ni(x,y) : Normalised gray level at pixel (x,y).• Mo and Vo : Desired mean and variance values

General Form of Gabor Filter:

f : frequency of the sinusoidal plane wave along the direction θ from the x-axis. δx’ and δy’ : space constants of the Gaussian envelope along x’ and y’ axes respectively.

00 , 22.50 , 450 , 67.50 filtered images

900 , 112.50 , 1350 , 157.5 0filtered images

Reconstructed image with eight filters.

Normalized image.

Accentute

Smoothen

inii

ii FyxF

nV ,

1

F i,θ(x ,y) :θ-direction filtered image for sector Si . Piθ is the mean of the pixel values of Fi,θ(x,y) in sector Si. ni is the number of pixels in sector Si.

Impression-2Impression-1

FingerCode of Impression-1 FingerCode of Impression-2

Generation of Fingercode:

Comparison of Euclidean distance. (For Matching)

1.High contrast 2.Faint Print. 3.Typical Wet print. Print.

4.Typical dry print.

5.Crease print.

6.Low contrast print.

Image Before Enhancement .

Image After Enhancement.

(very fine level features)

Image Before Thinning. Image After Thinning.

Detecting and filling gaps in ridge lines.

Minutiae-based representation may not be able to handle following situation.

Fingerprints from the same finger. Fingerprints from two different fingers.

Even the Ridge feature-based representation is not able to handle above situation.

Filterbank Method can comfortably handle such situation. Filterbank Method is faster. Use of FingerCode provide fast Matching.

Good accuracy & cost effective.

Even poor quality fingerprints can be handledby Filterbank Method.•Ability to work with incomplete input images.

This Method uses Local as well as Global features of fingerprint.

This technique can be amenable to any hardware implementation.

Susceptible to non-elastic distortions.Every stage in processing is dependent on one another.Small contact area of the sensors alleviates the deformation problem.Computationally expensive than the other methods.

1.Banking/Financial services. 2.Computer & IT Security. 3.Healthcare. 4.Immigration. 5.Law and Order. 6.Gatekeeper/Door Access Control. 7.Time and Attendance. 8.Consumer Products.

One of the latest use of the technology is on the Afghan- Pakistan border where the UN uses fingerprint verification to identify refugees. About 2000 refugees a day are being identified, it is hoped the system will cut down on refugees fraudulently claiming more than one aid package.

Fingerprint technology is increasingly being used by the aviation industry. New projects include fingerprint recognition of over 1,600 staff at London City Airport and a system to identify customers for some United Airline flights from Heathrow to the US.

ID-card containing fingerprint is issued for identification in Brunei.

Optical sensors.Ultrasound sensors.Chip-based sensors.Thermal sensors. Integrated products.

Capacitive sensor[FingerTIP™ by Infineon]

Optical fingerprint sensor[Fingerprint Identification Unit

by Sony]

Electro-optical sensor [DELSY® CMOS sensor ]

Thermal sensor[FingerChip™ by ATMEL

(was: Thomson CSF)]

E-Field Sensor[FingerLoc™ by Authentec]

Physical Access Control System [BioGate Tower by Bergdata] [ID Mouse by Siemens]

[BioMouse™ Plus by American Biometric Company]

[TravelMate 740 by Compaq and Acer]

Keyboard [G 81-12000 by Cherry]

System including fingerprint sensor,smartcard reader anddisplay by DELSY

In this project, we provide you an overview of the fingerprint-based personal verification. We outline some of the important issues involved in the design of fingerprint based identification systems and present algorithms for fingerprint feature extraction, enhancement, and matching. Our technique exploits both the local and global characteristics in a fingerprint image to verify an identity. The matching stage computes the Euclidean distance between the template FingerCode and the input FingerCode. The primary advantage of our approach is its computationally attractive matching capability.

Reference Books:1.’Handbook of Fingerprint Recognition’ by Anil.K.Jain,Lin Hong.

2.’ Fingerprint Image Enhancement:Algorithms &

Performance Evaluation’ by Arun Ross, Salil Prabhakar,Sharath Pankanti.

3.’Automated Fingerprint Identification & Imaging Systems’ by Sharath Pankanti, Anil.K.Jain.

www.veritouch.comwww.mathworks.comwww.biometricsystems.com