7,009 research outputs found
Two-stage quality adaptive fingerprint image enhancement using Fuzzy c-means clustering based fingerprint quality analysis
Fingerprint recognition techniques are immensely dependent on quality of the
fingerprint images. To improve the performance of recognition algorithm for
poor quality images an efficient enhancement algorithm should be designed.
Performance improvement of recognition algorithm will be more if enhancement
process is adaptive to the fingerprint quality (wet, dry or normal). In this
paper, a quality adaptive fingerprint enhancement algorithm is proposed. The
proposed fingerprint quality assessment algorithm clusters the fingerprint
images in appropriate quality class of dry, wet, normal dry, normal wet and
good quality using fuzzy c-means technique. It considers seven features namely,
mean, moisture, variance, uniformity, contrast, ridge valley area uniformity
and ridge valley uniformity into account for clustering the fingerprint images
in appropriate quality class. Fingerprint images of each quality class undergo
through a two-stage fingerprint quality enhancement process. A quality adaptive
preprocessing method is used as front-end before enhancing the fingerprint
images with Gabor, short term Fourier transform and oriented diffusion
filtering based enhancement techniques. Experimental results show improvement
in the verification results for FVC2004 datasets. Significant improvement in
equal error rate is observed while using quality adaptive preprocessing based
approaches in comparison to the current state-of-the-art enhancement
techniques.Comment: 34 pages, 8 figures, Submitted to Image and Vision Computin
An Iterative Fingerprint Enhancement Algorithm Based on Accurate Determination of Orientation Flow
We describe an algorithm to enhance and binarize a fingerprint image. The
algorithm is based on accurate determination of orientation flow of the ridges
of the fingerprint image by computing variance of the neighborhood pixels
around a pixel in different directions. We show that an iterative algorithm
which captures the mutual interdependence of orientation flow computation,
enhancement and binarization gives very good results on poor quality images.Comment: 10 pages, 4 figures. Ongoing work. To be submitted to appropriate
conference/journa
An Effective Method for Fingerprint Classification
This paper presents an effective method for fingerprint classification using
data mining approach. Initially, it generates a numeric code sequence for each
fingerprint image based on the ridge flow patterns. Then for each class, a seed
is selected by using a frequent itemsets generation technique. These seeds are
subsequently used for clustering the fingerprint images. The proposed method
was tested and evaluated in terms of several real-life datasets and a
significant improvement in reducing the misclassification errors has been
noticed in comparison to its other counterparts.Comment: 9 pages, 7 figures, 6 tables referred journal publication. arXiv
admin note: substantial text overlap with arXiv:1211.450
A Stable Minutia Descriptor based on Gabor Wavelet and Linear Discriminant Analysis
The minutia descriptor which describes characteristics of minutia, plays a
major role in fingerprint recognition. Typically, fingerprint recognition
systems employ minutia descriptors to find potential correspondence between
minutiae, and they use similarity between two minutia descriptors to calculate
overall similarity between two fingerprint images. A good minutia descriptor
can improve recognition accuracy of fingerprint recognition system and largely
reduce comparing time. A good minutia descriptor should have high ability to
distinguish between different minutiae and at the same time should be robust in
difficult conditions including poor quality image and small size image. It also
should be effective in computational cost of similarity among descriptors. In
this paper, a robust minutia descriptor is constructed using Gabor wavelet and
linear discriminant analysis. This minutia descriptor has high distinguishing
ability, stability and simple comparing method. Experimental results on FVC2004
and FVC2006 databases show that the proposed minutia descriptor is very
effective in fingerprint recognition
Minutiae Extraction from Fingerprint Images - a Review
Fingerprints are the oldest and most widely used form of biometric
identification. Everyone is known to have unique, immutable fingerprints. As
most Automatic Fingerprint Recognition Systems are based on local ridge
features known as minutiae, marking minutiae accurately and rejecting false
ones is very important. However, fingerprint images get degraded and corrupted
due to variations in skin and impression conditions. Thus, image enhancement
techniques are employed prior to minutiae extraction. A critical step in
automatic fingerprint matching is to reliably extract minutiae from the input
fingerprint images. This paper presents a review of a large number of
techniques present in the literature for extracting fingerprint minutiae. The
techniques are broadly classified as those working on binarized images and
those that work on gray scale images directly.Comment: 12 pages; IJCSI International Journal of Computer Science Issues,
Vol. 8, Issue 5, September 201
An Effective Fingerprint Classification and Search Method
This paper presents an effective fingerprint classification method designed
based on a hierarchical agglomerative clustering technique. The performance of
the technique was evaluated in terms of several real-life datasets and a
significant improvement in reducing the misclassification error has been
noticed. This paper also presents a query based faster fingerprint search
method over the clustered fingerprint databases. The retrieval accuracy of the
search method has been found effective in light of several real-life databases.Comment: 10 pages, 8 figures, 6 tables, referred journal publicatio
Bio-Authentication based Secure Transmission System using Steganography
Biometrics deals with identity verification of an individual by using certain
physiological or behavioral features associated with a person. Biometric
identification systems using fingerprints patterns are called AFIS (Automatic
Fingerprint Identification System). In this paper a composite method for
Fingerprint recognition is considered using a combination of Fast Fourier
Transform (FFT) and Sobel Filters for improvement of a poor quality fingerprint
image. Steganography hides messages inside other messages in such a way that an
"adversary" would not even know a secret message were present. The objective of
our paper is to make a bio-secure system. In this paper bio-authentication has
been implemented in terms of finger print recognition and the second part of
the paper is an interactive steganographic system hides the user's data by two
options- creating a songs list or hiding the data in an image.Comment: IEEE Publication format, International Journal of Computer Science
and Information Security, IJCSIS, Vol. 8 No. 1, April 2010, USA. ISSN 1947
5500, http://sites.google.com/site/ijcsis
Fingerprint Recognition Using Minutia Score Matching
The popular Biometric used to authenticate a person is Fingerprint which is
unique and permanent throughout a person's life. A minutia matching is widely
used for fingerprint recognition and can be classified as ridge ending and
ridge bifurcation. In this paper we projected Fingerprint Recognition using
Minutia Score Matching method (FRMSM). For Fingerprint thinning, the Block
Filter is used, which scans the image at the boundary to preserves the quality
of the image and extract the minutiae from the thinned image. The false
matching ratio is better compared to the existing algorithm.Comment: 8 Page
Automated Latent Fingerprint Recognition
Latent fingerprints are one of the most important and widely used evidence in
law enforcement and forensic agencies worldwide. Yet, NIST evaluations show
that the performance of state-of-the-art latent recognition systems is far from
satisfactory. An automated latent fingerprint recognition system with high
accuracy is essential to compare latents found at crime scenes to a large
collection of reference prints to generate a candidate list of possible mates.
In this paper, we propose an automated latent fingerprint recognition algorithm
that utilizes Convolutional Neural Networks (ConvNets) for ridge flow
estimation and minutiae descriptor extraction, and extract complementary
templates (two minutiae templates and one texture template) to represent the
latent. The comparison scores between the latent and a reference print based on
the three templates are fused to retrieve a short candidate list from the
reference database. Experimental results show that the rank-1 identification
accuracies (query latent is matched with its true mate in the reference
database) are 64.7% for the NIST SD27 and 75.3% for the WVU latent databases,
against a reference database of 100K rolled prints. These results are the best
among published papers on latent recognition and competitive with the
performance (66.7% and 70.8% rank-1 accuracies on NIST SD27 and WVU DB,
respectively) of a leading COTS latent Automated Fingerprint Identification
System (AFIS). By score-level (rank-level) fusion of our system with the
commercial off-the-shelf (COTS) latent AFIS, the overall rank-1 identification
performance can be improved from 64.7% and 75.3% to 73.3% (74.4%) and 76.6%
(78.4%) on NIST SD27 and WVU latent databases, respectively
Perfect Fingerprint Orientation Fields by Locally Adaptive Global Models
Fingerprint recognition is widely used for verification and identification in
many commercial, governmental and forensic applications. The orientation field
(OF) plays an important role at various processing stages in fingerprint
recognition systems. OFs are used for image enhancement, fingerprint alignment,
for fingerprint liveness detection, fingerprint alteration detection and
fingerprint matching. In this paper, a novel approach is presented to globally
model an OF combined with locally adaptive methods. We show that this model
adapts perfectly to the 'true OF' in the limit. This perfect OF is described by
a small number of parameters with straightforward geometric interpretation.
Applications are manifold: Quick expert marking of very poor quality (for
instance latent) OFs, high fidelity low parameter OF compression and a direct
road to ground truth OFs markings for large databases, say. In this
contribution we describe an algorithm to perfectly estimate OF parameters
automatically or semi-automatically, depending on image quality, and we
establish the main underlying claim of high fidelity low parameter OF
compression
- …