17,258 research outputs found
DWT Based Fingerprint Recognition using Non Minutiae Features
Forensic applications like criminal investigations, terrorist identification
and National security issues require a strong fingerprint data base and
efficient identification system. In this paper we propose DWT based Fingerprint
Recognition using Non Minutiae (DWTFR) algorithm. Fingerprint image is
decomposed into multi resolution sub bands of LL, LH, HL and HH by applying 3
level DWT. The Dominant local orientation angle {\theta} and Coherence are
computed on LL band only. The Centre Area Features and Edge Parameters are
determined on each DWT level by considering all four sub bands. The comparison
of test fingerprint with database fingerprint is decided based on the Euclidean
Distance of all the features. It is observed that the values of FAR, FRR and
TSR are improved compared to the existing algorithm.Comment: 9 page
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
PoreNet: CNN-based Pore Descriptor for High-resolution Fingerprint Recognition
With the development of high-resolution fingerprint scanners, high-resolution
fingerprint-based biometric recognition has received increasing attention in
recent years. This paper presents a pore feature-based approach for biometric
recognition. Our approach employs a convolutional neural network (CNN) model,
DeepResPore, to detect pores in the input fingerprint image. Thereafter, a
CNN-based descriptor is computed for a patch around each detected pore.
Specifically, we have designed a residual learning-based CNN, referred to as
PoreNet that learns distinctive feature representation from pore patches. For
verification, the match score is generated by comparing pore descriptors
obtained from a pair of fingerprint images in bi-directional manner using the
Euclidean distance. The proposed approach for high-resolution fingerprint
recognition achieves 2.91% and 0.57% equal error rates (EERs) on partial (DBI)
and complete (DBII) fingerprints of the benchmark PolyU HRF dataset. Most
importantly, it achieves lower FMR1000 and FMR10000 values than the current
state-of-the-art approach on both the datasets.Comment: 7 pages, 4 figures, 6table
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
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
Fingerprint Extraction Using Smartphone Camera
In the previous decade, there has been a considerable rise in the usage of
smartphones.Due to exorbitant advancement in technology, computational speed
and quality of image capturing has increased considerably. With an increase in
the need for remote fingerprint verification, smartphones can be used as a
powerful alternative for fingerprint authentication instead of conventional
optical sensors. In this research, wepropose a technique to capture
finger-images from the smartphones and pre-process them in such a way that it
can be easily matched with the optical sensor images.Effective finger-image
capturing, image enhancement, fingerprint pattern extraction, core point
detection and image alignment techniques have been discussed. The proposed
approach has been validated on FVC 2004 DB1 & DB2 dataset and the results show
the efficacy of the methodology proposed. The method can be deployed for
real-time commercial usage.Comment: 11 page
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
Automatic Dataset Annotation to Learn CNN Pore Description for Fingerprint Recognition
High-resolution fingerprint recognition often relies on sophisticated
matching algorithms based on hand-crafted keypoint descriptors, with pores
being the most common keypoint choice. Our method is the opposite of the
prevalent approach: we use instead a simple matching algorithm based on robust
local pore descriptors that are learned from the data using a CNN. In order to
train this CNN in a fully supervised manner, we describe how the automatic
alignment of fingerprint images can be used to obtain the required training
annotations, which are otherwise missing in all publicly available datasets.
This improves the state-of-the-art recognition results for both partial and
full fingerprints in a public benchmark. To confirm that the observed
improvement is due to the adoption of learned descriptors, we conduct an
ablation study using the most successful pore descriptors previously used in
the literature. All our code is available at
https://github.com/gdahia/high-res-fingerprint-recognitio
Performance of the Fuzzy Vault for Multiple Fingerprints (Extended Version)
The fuzzy vault is an error tolerant authentication method that ensures the
privacy of the stored reference data. Several publications have proposed the
application of the fuzzy vault to fingerprints, but the results of subsequent
analyses indicate that a single finger does not contain sufficient information
for a secure implementation. In this contribution, we present an implementation
of a fuzzy vault based on minutiae information in several fingerprints aiming
at a security level comparable to current cryptographic applications. We
analyze and empirically evaluate the security, efficiency, and robustness of
the construction and several optimizations. The results allow an assessment of
the capacity of the scheme and an appropriate selection of parameters. Finally,
we report on a practical simulation conducted with ten users.Comment: This article represents the full paper of a short version to appear
in the Proceedings of BIOSIG 2010 (copyright of Gesellschaft f\"ur
Informatik
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
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