Poster BZ4 11

Information sheet

A4 size

(21 cm x 29.7 cm)

Information sheet

A4 size

(21 cm x 29.7 cm)

Information sheet

A4 size

(21 cm x 29.7 cm)

Project Overview Methodology

Shrikant Patnaik, Kannan Chandrasegaran ,

Sagar Nilesh Shah & Saurabh Swarup

Prof Bing Zeng, ECE

Prof Cunsheng Ding, CSE

Unlike images from a traditional fingerprint scanner, fingertip photos

obtained from a webcam suffer from low contrast, high noise, and blurring.

We present a system that performs matching using such images.

Fingerprint registration and authentication is performed via a web interface.

The images are sent to a server where an image enhancement module

extracts the fingerprint. A matching module then computes a similarity

score for each pair.

INTERNET TRANSACTION SECURITY WITH

FINGERPRINT RECOGNITION

(BZ4/CD3)

CONTROL

CENTER

JAVA

FINGERTIP CAPTURE

Flash Webcam Capture

PHP Web Application

FINGERPRINT

EXTRACTION

MATLAB

DB

MySQL

ENROLLMENT

AUTHENTICATION

Send fingertip Image

Send fingerprint Image

Retrieve user and template Information

Store user and template Information

Send fingertip image

Get authentication/ enrollment response

NBIS SERVER - LINUX OS

CLIENT - BROWSER

Results

Client side :

•Acquire the low resolution

fingertip image from the user,

perform basic cropping, and

send it to the server securely.

Server application:

•Perform Image processing, converting a

fingertip image to a fingerprint image

•Perform Authentication using a fingerprint

matching module.

We have separated our system into two halves: client-side and server-side.

System Design

Registration Authentication

Capture 3 Enrollment Images

Server Upload

Image Processing and Enhancement

Minutiae Detection

Storage in Database

based on Username

Thresholding Function

Matching score with

all Templates

Retrieval of Relevant

Templates

Capture 1 Authentication Image

Flow Diagram

Results after each step of Image Enhancement (from top left):-

1. Original fingertip image 2. Grayscale Image 3. Lucy-

Richardson deconvolution 4. Unsharpening 5. Bandpass filtering

6. Normalization 7. Ridge frequency 8. Orientation image 9. Ridge

filter 10. Mask after hole filling 11. Masked image 12. Final image

In testing the performance of any fingerprint matching system, two types of

errors need to be considered - False Non-Match Rate (FNMR) is the rate at

which a genuine user is rejected by the system and False Match Rate (FMR)

is the rate at which an impostor is accepted by the system.

There is a natural tradeoff between these two values in any system, as having

a more liberal acceptance policy will inevitably accept more impostors, and

vice versa. To evaluate a system in general, the Equal Error Rate (EER),

defined as the point at which FMR = FNMR, may be used.

0.0000

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

0 20 40 60 80 100 120 140 160 180 200

R

a

t

e

Threshold value

3 Sample FMR

3 Sample FNMR

We found that 3 Enrollment Samples were ideal as it gave the lowest EER of

6.17%.

When False Match Rate

is set at 1% (which

means there is a 1%

chance of an impostor

breaching the system)

the False Non Match

Rate (the chance of a

genuine user being

rejected) is only 12.5%.

We use four main NBIS

binaries for Matching .

•DJPEG and CJPEG, which

convert a MATLAB processed

image to a NBIS compatible

grey scale image.

•MINDTCT for minutiae

detection.

•BOZORTH3 for fingerprint

matching.

Conclusion Our system has a number of merits that give it a significant edge over other

authentication systems :

•The system adds an additional layer of security by implementing the

webcam based fingerprint authentication system.

•As the registration and authentication system is accessible from a browser, it

can be instantly deployed to any user who has access to a computer, a

webcam, and a browser.

•As the only required hardware component is a low resolution camera (640 x

480), the deployment cost is very low.

•As the bulk of the processing happens on the server, clients can be built for

any platform with great speed.

Fingerprint matching relies on a features known as minutiae

Ridge Ending Ridge Bifurcation

Lucy-Richardson Deconvolution

Unsharpening

Bandpass Filter

Ridge Orientation

Ridge Segmentation

Reliability Map Ridge Frequency

Hole Filling Ridge Filtering

Masking

Segmentation

Fingerprint Extraction

Fingerprint Matching