umesh synopsis 1
TRANSCRIPT
ABSTRACT
Overlapped Fingerprint Recognition is a complex pattern recognition problem. It is
difficult to design accurate algorithms capable of separating the overlapped fingerprint,
extracting salient features and matching them in a robust way. Latent fingerprints lifted from
crime scenes often contain overlapping prints, which are difficult to separate and match by
state-of-the-art fingerprint matchers. A few methods have been proposed to separate
overlapping fingerprints to enable fingerprint matchers to successfully match the component
fingerprints. These methods are limited by the accuracy of the estimated orientation field,
which is not reliable for poor quality overlapping latent fingerprints. In this proposed
method, we try to improve the robustness of overlapping fingerprints separation, particularly
for low quality images. Our algorithm reconstructs the orientation fields of component prints
by modelling fingerprint orientation fields and then correcting it using dictionary based
approach. In order to facilitate this, we utilize the orientation cues of component fingerprints,
which are manually marked by fingerprint examiners. This additional mark-up is acceptable
in forensics, where the first priority is to improve the latent matching accuracy. The proposed
orientation field estimation algorithm consists of an offline dictionary construction stage and
an online orientation field estimation stage.
In the offline stage, a set of good quality fingerprints of various pattern types (arch, loop,
and whorl) is manually selected and their orientation fields are used to construct a dictionary
of orientation patches. In the online stage, given a fingerprint image, its orientation field is
automatically estimated using model based and dictionary based approach.
The proposed method will not only work on simulated overlapping prints, but also on real
overlapped latent fingerprint images. The proposed algorithm can be more effective in
separating poor quality overlapping fingerprints and enhancing the matching accuracy of
overlapping fingerprints.
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1. INTRODUCTION
Fingerprint recognition or fingerprint authentication refers to the automated method of
verifying a match between two human fingerprints. Fingerprints are one of many forms of
biometrics used to identify an individual and verify their identity. Because of their
uniqueness and consistency over time, fingerprints have been used for over a century, more
recently becoming automated (i.e. a biometric) due to advancement in computing
capabilities.
Typically, the input image contains only a single fingerprint. However, in practice,
particularly in forensics, two or more fingerprints could overlay on top of each other,
resulting in an overlapped fingerprint image. Available fingerprint matchers, however,
cannot accurately match overlapping fingerprints, because they assume that a fingerprint
image contains only a single fingerprint and hence single orientation field. Our interest here
is to develop algorithms to separate overlapping latents that will serve as a valuable tool in
forensics. Note that in forensics, the matching accuracy of latents is extremely critical even if
it involves some degree of manual intervention by latent examiners, including manual
markup.
1.1 Objective of the project
The objective of this research is to find a robust solution for separating the overlapped
fingerprint particularly for latent fingerprint which is difficult to separate using existing
techniques.
1.2 Scope of the project
The proposed method will serve a valuable tool in forensics for separation of
overlapped simulated fingerprint, inked fingerprint and also on latent overlapped fingerprints.
2. LITERATURE SURVEY
Fan et al. [3] proposed an algorithm to separate overlapped fingerprints based on image
enhancement using manually marked orientation field. However, it is very tedious and time-
consuming for the user to manually mark the orientation field of each component fingerprint
in the overlapped fingerprint image. Geng et al. [4] proposed to use morphological
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component analysis to separate overlapped fingerprints. However, their experiment shows
that this algorithm can only separate the component fingerprint which dominates the
overlapped image. chen et al. [1] In this paper present an algorithm to separate overlapped
fingerprints: The algorithm consists of three steps: 1) Initial orientation field is estimated
using local Fourier analysis. 2) Relaxation labeling method is employed to label the initial
orientation field into two classes. 3) Two component fingerprints are separated by enhancing
the overlapped fingerprint image using Gabor filters tuned to these two component
orientation fields.
Problem with approach is it does not perform very well when the singularity region of
component fingerprint overlapped because the relaxation algorithm solely based on local
continuity of orientation field.
shi et al. [2] propose overlap orientation field method based on constrained relaxation
labeling similar to chen et al. [1]. This algorithm is differing from chen et al[1] in following
aspects.1) Utilization of non-overlapped area. In this work non-overlapped area is utilized as
important constraints during relaxation labeling process. 2) Mutual exclusion constraint. We
treat each overlapped block as a single object rather than two ones to strictly enforce the
mutual exclusion constraint, namely, two candidate orientations in an overlapped block
cannot belong to the same fingerprint. 3) Order of updating label probabilities. We
sequentially update label probabilities in an overlapped block in an ascending order of the
distance between itself and non overlapped area. While in [1], the label probabilities in all
overlapped blocks are updated in parallel. Feng et al. [10] As this paper is similar to shi et al.
[2] but the difference is improved versions of the basic algorithm for two special but frequent
cases: 1) The mated template fingerprint of one component fingerprint is known and
2) The two component fingerprints are from the same finger.
2.1.8 zhao et al.[3] This approach is almost overcome the drawbacks of relaxation labelling
technique. In this paper, instead of separating the estimated mixed orientation field, they
reconstruct the orientation fields of component fingerprints via modelling orientation fields
and then predicting unknown orientation fields based on a small number of manually marked
orientation cues in fingerprints. This model based method significantly improves the
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accuracy of overlapping fingerprints separation, especially for the practical scenario of poor
quality overlapped latent images.
3. PROPOSED METHODOLOGY
The proposed orientation field estimation algorithm consists of an offline dictionary
construction stage and an online orientation field estimation stage illustrated in fig. 3.1.
In the offline stage, a set of good quality fingerprints of various pattern types (arch, loop,
and whorl) is manually selected and their orientation fields are used to construct a dictionary
of orientation patches. In the online stage, given a fingerprint image, its orientation field is
automatically estimated. The figure 3.1 shows the online and offline stage in detail.
Figure.3.1 Proposed system Overview
3.1 Offline Dictionary construction
The dictionary consists of a number of orientation patches of the same size. The numbers of
orientation patches, whose orientation elements are all available, are obtained by sliding a
window.
The greedy algorithm for dictionary construction is described below in Table 3.1:
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Reference Fingerprints
Reference Orientation
fields
Input Fingerprint
Initial Orientation
Field
Dictionary of orientation
patches
Corrected orientation field
Orientation
Field Estimation
Dictionary
Construction
Orientation Estimation
DictionaryLookup
Context based correction
Online
Offline
Gabor
Filter
Step Description1 The first orientation patch is added into the dictionary, which is initially empty.
2 Then we test whether the next orientation patch is sufficiently different from all the orientation patches in the dictionary. If yes, it is also added into the dictionary; otherwise, the next orientation patch is tested
3 Repeat step 2 until all orientation patches has been tested.Table 3.1 Dictionary Construction.[11]
3.2 Online stage
In the online stage, given a fingerprint image, its orientation field is automatically estimated,
the steps for online is described in table 3.2.
Step Description1 Initial estimation. The initial orientation field is obtained using a Model based
technique [9].2 Dictionary lookup. The initial orientation field is divided into overlapping
patches. For each initial orientation patch, its six nearest neighbours in the dictionary are viewed as candidates for replacing the noisy initial orientation patch.
3 Context-based correction. The optimal combination of candidate orientation patches is found by considering the compatibility between neighbouring orientation patches.
Table 3.2. Online Stage.
3.2.1 Initial orientation field estimation of two component fingerprint
The initial orientation field is obtained using a Model based technique [9]. The following
parameters are used in algorithm to find the orientation field.
DescriptionRegion Segmentation by manual marking singular points, orientation cues and overlapped region.
Modelling orientation field using combination model of zero pole and polynomial model. Zero pole is use to detect area near singular point and polynomial model is used to detect remaining area. The zero pole model is
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We first remove the influence of singular points from the orientation field by subtracting from, and then approximate the residual orientation field with a set of basis functions.
(2)
The cosine and sine components of the doubled residual orientations can be approximated by
(3)
Given the residual orientation field in the region of interest, the coefficients can be obtained by solving the following minimization problems using least squares optimization.
(4)
Step 3) The residual orientation field can be calculated as:
(5)
Finally, the orientation at is obtained by adding back the influence of singular points to the estimated residual orientation, i.e.,
(6)
3.2.2 Reconstruct the orientation field using algorithm
In the reconstructing process the orientation field is reconstructed using combination model.
The zero pole and polynomial model is used as a combination model.
Steps DescriptionInput: Region of interest; θ(ΩC): Orientation cues in ΩC ;SP: Singular points
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Output:
: Reconstructed Orientation field in Ω
1 Compute the orientation fields of the singular point according to(1):
2 Compute the residual orientation fields in ΩC:
3 Initialize the prediction area:
4 While is not empty do
5 Estimate the model coefficients based on according to (4)
6 Compute the predicted residual orientations in according to (5):
7 Regularize the residual orientations in :
8 ,
9 end while
10 Compute the reconstructed orientation field according to (6):
.
Table 3.3 Reconstructions process [9]
3.3 Dictionary lookup
The similarity between an initial orientation patch and a reference orientation patch is
computed by comparing corresponding orientation elements. Hence, a candidate is selected
from initial candidates using the following greedy strategy described in table 3.4.
Step Description
1 Choose the first initial candidate.
2 The next initial candidate is compared to each of the chosen candidates. If its
similarity to all the chosen candidates is below a predefined threshold it is chosen.
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It is defined as: S(Θ,ɸ)=ns/nf.
3 Repeat step 2 for all the initial candidates until candidates have been chosen or all
initial candidates have been checked
Table 3.4 Dictionary Lookup
3.4 Context-based orientation field correction
After dictionary lookup, we obtain a list of candidate orientation patches, for an initial
orientation patch. To resolve the ambiguity, i.e., determine a single candidate for each patch,
contextual information needs to be utilized. We address this problem by searching for a set of
candidates, r, which minimizes an energy function. We consider two factors in designing the
energy function: 1) The similarity between the reference orientation patches and the
corresponding initial orientation patches, and 2) The compatibility between neighboring
reference orientation patches. The energy function is defined as:
Where Es denotes the similarity term, Ec denotes the compatibility term, and Wc is the weight of compatibility term shown in table 3.5.
The similarity term is defined as:
The compatibility term is defined as:
The compatibility is computed as:
Table 3.5 Context based correction [11]
After this step of context based correction we get the orientation field of two component
fingerprint.
3.5 Separating overlapped fingerprint
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Two important parameters of 2D Gabor filters are local ridge orientation and frequency.
Based on the component ridge orientation field, we estimate the ridge frequency map of each
component fingerprint using the method proposed in [5]. When the ridge orientation field and
ridge frequency map are obtained, Gabor filtering can connect broken ridges and remove
intervening ridges. The main steps of the algorithm include: 1) Local orientation estimation:
The orientation image is estimated from the normalized input fingerprint image. 2) Local
frequency estimation: The frequency image is computed from the estimated orientation
image.3) Filtering: A bank of Gabor filters which is tuned to local ridge orientation and ridge
frequency is applied to the ridge-and-valley pixels in the normalized input fingerprint image
to obtain an enhanced fingerprint image. This is the proposal which can be efficiently used
for reconstruction and prediction model more accurately and precisely.
4. CONCLUSION
The robust and efficient overlapped fingerprint separation has been proposed .As finger print
orientation is very important step in separation algorithm; we have proposed a robust
orientation field estimation algorithm for latent fingerprint enhancement. There are several
advantages of the proposed fingerprint separation method. The method proposed not only
effective for simulated and inked fingerprint but also effective for latent fingerprint
separation. In the proposed system we are using model based technique and dictionary based
approach for initial orientation estimation which is inspired from spelling correction
techniques in natural language processing, as state of art method of relaxation labeling
algorithm used local fourier analysis which was the bottle neck. So here we are using model
based orientation estimation technique. The proposed algorithm reconstructs the orientation
field of overlapping fingerprints based on a set of manually marked features, including
regions of interest, singular points, and orientation cues. Based on the underlying model of
fingerprint ridge orientation field, the proposed method can simultaneously predict unknown
orientations in fingerprints and use the dictionary based approach for context based
correction. and finally the two component fingerprints are separated by filtering the
overlapped fingerprint image using Gabor filters tuned to the component orientation fields.
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