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A project abstract
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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