4,990 research outputs found
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
ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction
Performing recognition tasks using latent fingerprint samples is often
challenging for automated identification systems due to poor quality,
distortion, and partially missing information from the input samples. We
propose a direct latent fingerprint reconstruction model based on conditional
generative adversarial networks (cGANs). Two modifications are applied to the
cGAN to adapt it for the task of latent fingerprint reconstruction. First, the
model is forced to generate three additional maps to the ridge map to ensure
that the orientation and frequency information is considered in the generation
process, and prevent the model from filling large missing areas and generating
erroneous minutiae. Second, a perceptual ID preservation approach is developed
to force the generator to preserve the ID information during the reconstruction
process. Using a synthetically generated database of latent fingerprints, the
deep network learns to predict missing information from the input latent
samples. We evaluate the proposed method in combination with two different
fingerprint matching algorithms on several publicly available latent
fingerprint datasets. We achieved the rank-10 accuracy of 88.02\% on the
IIIT-Delhi latent fingerprint database for the task of latent-to-latent
matching and rank-50 accuracy of 70.89\% on the IIIT-Delhi MOLF database for
the task of latent-to-sensor matching. Experimental results of matching
reconstructed samples in both latent-to-sensor and latent-to-latent frameworks
indicate that the proposed method significantly increases the matching accuracy
of the fingerprint recognition systems for the latent samples.Comment: Accepted in BTAS 201
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 Efficient Automatic Attendance System Using Fingerprint Reconstruction Technique
Biometric time and attendance system is one of the most successful
applications of biometric technology. One of the main advantage of a biometric
time and attendance system is it avoids "buddy-punching". Buddy punching was a
major loophole which will be exploiting in the traditional time attendance
systems. Fingerprint recognition is an established field today, but still
identifying individual from a set of enrolled fingerprints is a time taking
process. Most fingerprint-based biometric systems store the minutiae template
of a user in the database. It has been traditionally assumed that the minutiae
template of a user does not reveal any information about the original
fingerprint. This belief has now been shown to be false; several algorithms
have been proposed that can reconstruct fingerprint images from minutiae
templates. In this paper, a novel fingerprint reconstruction algorithm is
proposed to reconstruct the phase image, which is then converted into the
grayscale image. The proposed reconstruction algorithm reconstructs the phase
image from minutiae. The proposed reconstruction algorithm is used to automate
the whole process of taking attendance, manually which is a laborious and
troublesome work and waste a lot of time, with its managing and maintaining the
records for a period of time is also a burdensome task. The proposed
reconstruction algorithm has been evaluated with respect to the success rates
of type-I attack (match the reconstructed fingerprint against the original
fingerprint) and type-II attack (match the reconstructed fingerprint against
different impressions of the original fingerprint) using a commercial
fingerprint recognition system. Given the reconstructed image from our
algorithm, we show that both types of attacks can be effectively launched
against a fingerprint recognition system.Comment: 6pages,5figure
A Fully Automated Latent Fingerprint Matcher with Embedded Self-learning Segmentation Module
Latent fingerprint has the practical value to identify the suspects who have
unintentionally left a trace of fingerprint in the crime scenes. However,
designing a fully automated latent fingerprint matcher is a very challenging
task as it needs to address many challenging issues including the separation of
overlapping structured patterns over the partial and poor quality latent
fingerprint image, and finding a match against a large background database that
would have different resolutions. Currently there is no fully automated latent
fingerprint matcher available to the public and most literature reports have
utilized a specialized latent fingerprint matcher COTS3 which is not accessible
to the public. This will make it infeasible to assess and compare the relevant
research work which is vital for this research community. In this study, we
target to develop a fully automated latent matcher for adaptive detection of
the region of interest and robust matching of latent prints. Unlike the
manually conducted matching procedure, the proposed latent matcher can run like
a sealed black box without any manual intervention. This matcher consists of
the following two modules: (i) the dictionary learning-based region of interest
(ROI) segmentation scheme; and (ii) the genetic algorithm-based minutiae set
matching unit. Experimental results on NIST SD27 latent fingerprint database
demonstrates that the proposed matcher outperforms the currently public
state-of-art latent fingerprint matcher
Generative Convolutional Networks for Latent Fingerprint Reconstruction
Performance of fingerprint recognition depends heavily on the extraction of
minutiae points. Enhancement of the fingerprint ridge pattern is thus an
essential pre-processing step that noticeably reduces false positive and
negative detection rates. A particularly challenging setting is when the
fingerprint images are corrupted or partially missing. In this work, we apply
generative convolutional networks to denoise visible minutiae and predict the
missing parts of the ridge pattern. The proposed enhancement approach is tested
as a pre-processing step in combination with several standard feature
extraction methods such as MINDTCT, followed by biometric comparison using MCC
and BOZORTH3. We evaluate our method on several publicly available latent
fingerprint datasets captured using different sensors
Automated Region Masking Of Latent Overlapped Fingerprints
Fingerprints have grown to be the most robust and efficient means of
biometric identification. Latent fingerprints are commonly found at crime
scenes. They are also of the overlapped kind making it harder for
identification and thus the separation of overlapped fingerprints has been a
conundrum to surpass. The usage of dedicated software has resulted in a manual
approach to region masking of the two given overlapped fingerprints. The region
masks are then further used to separate the fingerprints. This requires the
user's physical concentration to acquire the separate region masks, which are
found to be time-consuming. This paper proposes a novel algorithm that is fully
automated in its approach to region masking the overlapped fingerprint image.
The algorithm recognizes a unique approach of using blurring, erosion, and
dilation in order to attain the desired automated region masks. The experiments
conducted visually demonstrate the effectiveness of the algorithm.Comment: Accepted and presented in I-PACT international IEEE conference on
21st and 22nd Apri
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
FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction
Minutiae extraction is of critical importance in automated fingerprint
recognition. Previous works on rolled/slap fingerprints failed on latent
fingerprints due to noisy ridge patterns and complex background noises. In this
paper, we propose a new way to design deep convolutional network combining
domain knowledge and the representation ability of deep learning. In terms of
orientation estimation, segmentation, enhancement and minutiae extraction,
several typical traditional methods performed well on rolled/slap fingerprints
are transformed into convolutional manners and integrated as an unified plain
network. We demonstrate that this pipeline is equivalent to a shallow network
with fixed weights. The network is then expanded to enhance its representation
ability and the weights are released to learn complex background variance from
data, while preserving end-to-end differentiability. Experimental results on
NIST SD27 latent database and FVC 2004 slap database demonstrate that the
proposed algorithm outperforms the state-of-the-art minutiae extraction
algorithms. Code is made publicly available at:
https://github.com/felixTY/FingerNet
Performance Measurement and Method Analysis (PMMA) for Fingerprint Reconstruction
Fingerprint reconstruction is one of the most well-known and publicized
biometrics. Because of their uniqueness and consistency over time, fingerprints
have been used for identification over a century, more recently becoming
automated due to advancements in computed capabilities. Fingerprint
reconstruction is popular because of the inherent ease of acquisition, the
numerous sources (e.g. ten fingers) available for collection, and their
established use and collections by law enforcement and immigration.
Fingerprints have always been the most practical and positive means of
identification. Offenders, being well aware of this, have been coming up with
ways to escape identification by that means. Erasing left over fingerprints,
using gloves, fingerprint forgery; are certain examples of methods tried by
them, over the years. Failing to prevent themselves, they moved to an extent of
mutilating their finger skin pattern, to remain unidentified. This article is
based upon obliteration of finger ridge patterns and discusses some known cases
in relation to the same, in chronological order; highlighting the reasons why
offenders go to an extent of performing such act. The paper gives an overview
of different methods and performance measurement of the fingerprint
reconstruction.Comment: 4pages,1 figure,1 tabl
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