3,634 research outputs found
End-to-End Latent Fingerprint Search
Latent fingerprints are one of the most important and widely used sources of
evidence in law enforcement and forensic agencies. Yet the performance of the
state-of-the-art latent recognition systems is far from satisfactory, and they
often require manual markups to boost the latent search performance. Further,
the COTS systems are proprietary and do not output the true comparison scores
between a latent and reference prints to conduct quantitative evidential
analysis. We present an end-to-end latent fingerprint search system, including
automated region of interest (ROI) cropping, latent image preprocessing,
feature extraction, feature comparison , and outputs a candidate list. Two
separate minutiae extraction models provide complementary minutiae templates.
To compensate for the small number of minutiae in small area and poor quality
latents, a virtual minutiae set is generated to construct a texture template. A
96-dimensional descriptor is extracted for each minutia from its neighborhood.
For computational efficiency, the descriptor length for virtual minutiae is
further reduced to 16 using product quantization. Our end-to-end system is
evaluated on three latent databases: NIST SD27 (258 latents); MSP (1,200
latents), WVU (449 latents) and N2N (10,000 latents) against a background set
of 100K rolled prints, which includes the true rolled mates of the latents with
rank-1 retrieval rates of 65.7%, 69.4%, 65.5%, and 7.6% respectively. A
multi-core solution implemented on 24 cores obtains 1ms per latent to rolled
comparison
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
An Effective Fingerprint Verification Technique
This paper presents an effective method for fingerprint verification based on
a data mining technique called minutiae clustering and a graph-theoretic
approach to analyze the process of fingerprint comparison to give a feature
space representation of minutiae and to produce a lower bound on the number of
detectably distinct fingerprints. The method also proving the invariance of
each individual fingerprint by using both the topological behavior of the
minutiae graph and also using a distance measure called Hausdorff distance.The
method provides a graph based index generation mechanism of fingerprint
biometric data. The self-organizing map neural network is also used for
classifying the fingerprints.Comment: Submitted to Journal of Computer Science and Engineering, see
http://sites.google.com/site/jcseuk/volume-1-issue-1-may-201
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
Latent Fingerprint Registration via Matching Densely Sampled Points
Latent fingerprint matching is a very important but unsolved problem. As a
key step of fingerprint matching, fingerprint registration has a great impact
on the recognition performance. Existing latent fingerprint registration
approaches are mainly based on establishing correspondences between minutiae,
and hence will certainly fail when there are no sufficient number of extracted
minutiae due to small fingerprint area or poor image quality. Minutiae
extraction has become the bottleneck of latent fingerprint registration. In
this paper, we propose a non-minutia latent fingerprint registration method
which estimates the spatial transformation between a pair of fingerprints
through a dense fingerprint patch alignment and matching procedure. Given a
pair of fingerprints to match, we bypass the minutiae extraction step and take
uniformly sampled points as key points. Then the proposed patch alignment and
matching algorithm compares all pairs of sampling points and produces their
similarities along with alignment parameters. Finally, a set of consistent
correspondences are found by spectral clustering. Extensive experiments on
NIST27 database and MOLF database show that the proposed method achieves the
state-of-the-art registration performance, especially under challenging
conditions
Latent Fingerprint Recognition: Role of Texture Template
We propose a texture template approach, consisting of a set of virtual
minutiae, to improve the overall latent fingerprint recognition accuracy. To
compensate for the lack of sufficient number of minutiae in poor quality latent
prints, we generate a set of virtual minutiae. However, due to a large number
of these regularly placed virtual minutiae, texture based template matching has
a large computational requirement compared to matching true minutiae templates.
To improve both the accuracy and efficiency of the texture template matching,
we investigate: i) both original and enhanced fingerprint patches for training
convolutional neural networks (ConvNets) to improve the distinctiveness of
descriptors associated with each virtual minutiae, ii) smaller patches around
virtual minutiae and a fast ConvNet architecture to speed up descriptor
extraction, iii) reduce the descriptor length, iv) a modified hierarchical
graph matching strategy to improve the matching speed, and v) extraction of
multiple texture templates to boost the performance. Experiments on NIST SD27
latent database show that the above strategies can improve the matching speed
from 11 ms (24 threads) per comparison (between a latent and a reference print)
to only 7.7 ms (single thread) per comparison while improving the rank-1
accuracy by 8.9% against 10K gallery
Fingerprint Distortion Rectification using Deep Convolutional Neural Networks
Elastic distortion of fingerprints has a negative effect on the performance
of fingerprint recognition systems. This negative effect brings inconvenience
to users in authentication applications. However, in the negative recognition
scenario where users may intentionally distort their fingerprints, this can be
a serious problem since distortion will prevent recognition system from
identifying malicious users. Current methods aimed at addressing this problem
still have limitations. They are often not accurate because they estimate
distortion parameters based on the ridge frequency map and orientation map of
input samples, which are not reliable due to distortion. Secondly, they are not
efficient and requiring significant computation time to rectify samples. In
this paper, we develop a rectification model based on a Deep Convolutional
Neural Network (DCNN) to accurately estimate distortion parameters from the
input image. Using a comprehensive database of synthetic distorted samples, the
DCNN learns to accurately estimate distortion bases ten times faster than the
dictionary search methods used in the previous approaches. Evaluating the
proposed method on public databases of distorted samples shows that it can
significantly improve the matching performance of distorted samples.Comment: Accepted at ICB 201
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
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 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
- …