07-08-2012, 03:25 PM
Fingerprint Identification and Verification System using Minutiae Matching
[attachment=30415]
INTRODUCTION
Fingerprints have been in use for biometric recognition
since long because of their high acceptability,
immutability and individuality. Immutability refers to the
persistence of the fingerprints over time whereas
individuality is related to the uniqueness of ridge details
across individuals. The probability that two fingerprints
are alike is 1 in 1.9 x 1015 [1]. These features make the use
of fingerprints extremely effective in areas where the
provision of a high degree of security is an issue. The
major steps involved in automated fingerprint recognition
include a) Fingerprint Acquisition, b) Fingerprint
Segmentation, c) Fingerprint Image Enhancement, d)
Feature Extraction e) Minutiae Matching, f) Fingerprint
Classification.
IMPLEMENTATION OF THE AFIS
This paper introduces a prototype automatic identity
authentication system that is capable of authenticating the
identity of an individual, using fingerprints. The main
components of the AFIS are
1. Fingerprint database
2. Fingerprint features database
3. Enrollment Module
Fingerprint Segmentation
Fingerprint segmentation is an important part of a
fingerprint identification and verification system. However
the time spent in segmentation is also crucial. The
algorithms presented in [13] and [14] work quite well in
the extraction of the required region but these algorithms
have very high computational cost. We have developed an
efficient algorithm that works with acceptable
performance and has a lower computational cost. This
algorithm is based only on the block coherence of an
image. Coherence gives us a measure of how well the
gradients of the fingerprint image are pointing in the same
direction. In a window of size WxW around a pixel, the
coherence is defined as:
Fingerprint Enhancement
The fingerprint enhancement algorithm mentioned in [20]
was found to be suitable for this application and was
therefore used in the system. Better results are obtained
using [22] but it is slightly more time consuming. This
algorithm calls for the development of a ridge frequency
image IRF and ridge orientation IRO image for a fingerprint.
Gabor filters are used to enhance the fingerprint utilizing
the ridge frequency and ridge orientation information
obtained from the frequency and orientation images
obtained earlier. The enhanced image IE is then binarized
using adaptive thresholding to give a binarized image IEB .
The binarized image is thinned to give IT. The thinned
version is used for minutiae extraction.(see figure 3)
Fingerprint Classification
Fingerprint classification was carried out by the extraction
of the singular points from the fingerprint image using the
approach presented by [41] (see figure 8). After the
extraction of singular points the approach mentioned in
[42] was used to perform a rule-based classification.
[attachment=30415]
INTRODUCTION
Fingerprints have been in use for biometric recognition
since long because of their high acceptability,
immutability and individuality. Immutability refers to the
persistence of the fingerprints over time whereas
individuality is related to the uniqueness of ridge details
across individuals. The probability that two fingerprints
are alike is 1 in 1.9 x 1015 [1]. These features make the use
of fingerprints extremely effective in areas where the
provision of a high degree of security is an issue. The
major steps involved in automated fingerprint recognition
include a) Fingerprint Acquisition, b) Fingerprint
Segmentation, c) Fingerprint Image Enhancement, d)
Feature Extraction e) Minutiae Matching, f) Fingerprint
Classification.
IMPLEMENTATION OF THE AFIS
This paper introduces a prototype automatic identity
authentication system that is capable of authenticating the
identity of an individual, using fingerprints. The main
components of the AFIS are
1. Fingerprint database
2. Fingerprint features database
3. Enrollment Module
Fingerprint Segmentation
Fingerprint segmentation is an important part of a
fingerprint identification and verification system. However
the time spent in segmentation is also crucial. The
algorithms presented in [13] and [14] work quite well in
the extraction of the required region but these algorithms
have very high computational cost. We have developed an
efficient algorithm that works with acceptable
performance and has a lower computational cost. This
algorithm is based only on the block coherence of an
image. Coherence gives us a measure of how well the
gradients of the fingerprint image are pointing in the same
direction. In a window of size WxW around a pixel, the
coherence is defined as:
Fingerprint Enhancement
The fingerprint enhancement algorithm mentioned in [20]
was found to be suitable for this application and was
therefore used in the system. Better results are obtained
using [22] but it is slightly more time consuming. This
algorithm calls for the development of a ridge frequency
image IRF and ridge orientation IRO image for a fingerprint.
Gabor filters are used to enhance the fingerprint utilizing
the ridge frequency and ridge orientation information
obtained from the frequency and orientation images
obtained earlier. The enhanced image IE is then binarized
using adaptive thresholding to give a binarized image IEB .
The binarized image is thinned to give IT. The thinned
version is used for minutiae extraction.(see figure 3)
Fingerprint Classification
Fingerprint classification was carried out by the extraction
of the singular points from the fingerprint image using the
approach presented by [41] (see figure 8). After the
extraction of singular points the approach mentioned in
[42] was used to perform a rule-based classification.