In the first layer, possible finger codes of latent fingerprint are recognized based on basic pattern features and then reordered to determine their database access priority.. In the
Trang 1An Efficient Cascaded System for Latent Fingerprint Recognition Nguyen Thi Huong Thuy1, Hoang Xuan Huan2, Nguyen Ngoc Ky1 and Le Minh Khoi2
1 General Department of Technique - Logistic, Vietnamese Ministry of Public Security
huongthuykta@yahoo.com, kynguyen22@gmail.com
2 Faculty of Information Technology, Vietnam National University – Hanoi, University of Engineering and Technology
huanhx@vnu.edu.vn, khoilm@vnu.edu.vn
Abstract - This paper proposes a cascaded scheme to improve
the efficiency of latent fingerprint identification system In this
scheme, the feature set of latent fingerprint such as finger codes,
basic patterns, ridge counts and minutia with their local
structures are sequentially exploited in four cascaded layers In
the first layer, possible finger codes of latent fingerprint are
recognized based on basic pattern features and then reordered to
determine their database access priority In the second layer, its
minutiae are extracted and assessed to affine matching in the
third layer In the fourth layer, any case having too many
candidates in the previous layer is further matched based on
exploiting local structure in order to downsizing the result list
On the verification layer, the minutiae information and local
structure of corresponding pairs are presented to human experts
for further verification Experimental results on C@FRIS
database show that our proposed method obtains high matching
accuracy and considerably low identification time
Keywords: AFIS, C@FRIS, latent fingerprint identification,
fingerprint verification, cascading, minutiae
I INTRODUCTION
Fingerprint-based identifications have been applied
efficiently in forensics applications over a century [4]
However, there are many open problems attracting researchers
in this field [1]-[5], [7]-[9] While verification and automatic
authentication for rolled and plain fingerprints have achieved
tremendous progress, latent fingerprint recognition faces
difficult problems [1], [7] Latent fingerprints from relative
careless inadvertent individuality on objects are usually of
poor quality because of noise and non-linear distortion
Therefore, it is difficult to match them In addition, in
Vietnam and many other countries, along with latent
fingerprints collected at crime scenes, databases mainly store
paper-thin fingerprints or scanned paper-thin fingerprints
which are more difficult to process than sensor fingerprints
In Automatic Fingerprint Identification System (AFIS),
matching two fingerprint images is one of the main tasks It
decides the performance of the system [7] In the literature,
many matching techniques have already been proposed [2],
[7] Nevertheless, the techniques yielding high performance
usually require large amount of search time
One of the prospective solutions to reduce the identification
time is to apply cascading technique which integrates many
algorithms from simple to complex into an AFIS [3], [13]
This paper proposes a 4-layers cascaded architecture for latent
fingerprint identification system In the first layer, latent
fingerprint is recognized by its possible fingers [10] based on
basic pattern features and then reordered (to determine the
database access priority on the third layer); In the second
layer, minutiae and their local ridge-valley structure will be
extracted and assessed for affine matching in the third layer
on P-TPS model improved from [9] will be applied to decide before verification P-TPS matching helps eliminate the non-linear distortion effectively In order to decrease identification time, fingerprints in database are organized and indexed by finger codes, basic fingerprint pattern Moreover, the minutiae matching process is parallelized on a computer cluster
Experimental results on the database C@FRIS DB show that our new proposed system provides better outcomes than that of the earlier version of C@FRIS (built by research group
of the Vietnamese Ministry of Public Security, awarded VIFOTEC 2008 and upgraded in 2009)
The rest of the paper is organized as follows Section 2 briefly introduces latent fingerprint recognition and some basic techniques for cascaded architecture The method applying P-TPS technique is described in Section 3 The scheme of the cascaded system and the organization of the database for parallelized searching are highlighted in Section
4 Section 5 presents experimental results compared with C@FRIS system Finally, Section 6 concludes the paper
II LATENT FINGERPRINT RECOGNITION AND RELATED WORKS
This section briefly introduces latent fingerprint identification system It also describes some techniques such
as finger recognition, fingerprint classification, matching minutiae, TPS warping model
A Latent fingerprint recognition and identification 1) Latent fingerprint matching problem
The latent fingerprint matching problem is described as follows: Given a query fingerprint Iq (latent) and a fingerprint database, it is to determine whether the database contains the genuine fingerprint of this query fingerprint or not If yes, the system will display it
Latent fingerprints in nature are often of poor quality and not complete as rolled/plain fingerprint Thus, it is difficult to use them as inputs for an automatic recognition system Therefore, the identification process is usually divided into two layers: identification by computer and visual verification
by human being [7] The identification layer aims at finding the fingerprint images in the database which are most similar
to Iq This layer is usually done by AFIS After having the images outputted by the first layer, the verification layer is to identify among them which one is genuine with Iq This layer
is often performed by human experts, either with or without computer assistance Examiners often stop once getting the first match
2) Latent fingerprint identification system
While automatic fingerprint identification systems work well with rolled/plain fingerprints, latent fingerprint
2013 IEEE RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for the Future (RIVF)
Trang 2attention from the research community [1], [2] In order to
solve the latent fingerprint problem with high accuracy and
decreased search time, it is necessary to combine different
techniques (see [13]) Classification and cascading is one of
the most efficient methods which help downsize the matched
fingerprint list from database Moreover, fast matching
method helps remove most dissimilar fingerprints with Iq to
focus on most similar fingerprints Specifically, a matching
method needs to effectively process non-linear distortion
This paper proposes an AFIS using cascaded architecture
with the following components: fingerprint classification,
finger recognition, parallelized minutiae matching based on
reasonable organizing database for matching and verification
assistance In the following sections, these components are
going to be briefly introduced
B Finger recognition based on fingerprint trace
In order to determine the finger or the order of possible
fingers of suspect which left the latent trace, K Nguyen Ngoc
[10] proposed a statistic method using maximum a posteriori
probability (MAP) for finger recognizing based on latent
fingerprint trace
Based on this method, the identification process can be
conducted first on the suspected fingerprint instead of all
fingerprints This significantly reduces the search time
C Fingerprint classification
In order to accelerate the identification process, fingerprint
images are often classified into basic patterns according to
local ridges and relative position of singular points [4], [6],
[11], [12] Identification only applies to fingerprints of the
same type with Iq in the database The FBI [7] proposed
classifying fingerprints into three basic patterns: the arch, the
loop and the whorl
In order to improve fingerprint classification performance
according to the FBI’s standard, Karu [4], [7] proposed a
solution using Poincare index for detecting minutiae
However, the drawback of this method is that it requires
complete fingerprint and local ridge orientation of core region
and delta region which need to be clear
To eliminate the limitations, Wang [11] proposed a
classification algorithm which is only based on core points
and directions around the core point Nevertheless, an exact
identification of the core points is required
Our proposed system combines the two methods of Karu
[4] and Wang [11] which are going to be described in Section
4.1 to classify a fingerprint
D Minutiae based fingerprint matching
Given a query fingerprint Iq and a template fingerprint It, it
is to find out whether they originate from the same finger or
not In all fingerprint matching algorithms [7], matching based
on minutiae is simple but yet efficient and therefore is widely
used
1) Minutiae based method
In a fingerprint image, points representing discontinuities
of fingerprint local structure such as end points, bifurcation points are called minutiae
In order to match fingerprints using sets of minutiae, two fingerprint images Iq, It have to be pre-processed by extracting and assessing features
2) Matching scheme based on minutiae
Let nt and nq be the number of minutiae on the query fingerprint and sample fingerprint, respectively Assume that there are n corresponding minutiae pairs found from two images The similarity of two fingerprint images is characterized by the measurement S(It,Iq) and given by the following formula:
S(I t ,I q ) = n 2 /(n t×n q ) (1)
The simplest and most general transformation used in matching methods to align two images is the affine transformation However, due to the nonlinear distorted nature
of latent fingerprints, the efficiency of this method is insufficient and is usually employed to determine initial corresponding minutiae pairs for advanced warping methods [2], [7] One of widely used warping transformations is Thin-Plate-Spline (TPS) deformation model in [5]
E Thin-Plate-Spline deformation model
After having determined the n pairs of corresponding minutiae by using affine transformations for creating an initial set of landmark points, our system warps the image by the TPS model [5], [7]
III PARTIAL TPS FINGERPRINT MATCHING METHOD
In order to deal with the non-linear distortion problem, the authors in [9] proposed a partial TPS warping method using
an additional technique to enrich the set of landmark points by appending more pseudo-minutiae belonging to the associated ridge-valley pair Our experimental results verified that this matching based on local point model helps solve the non-linear distorted problem efficiently In this paper, P-TPS technique is used for building improved matching algorithm for doubtful fingerprint image pairs which are the outcomes of the affine matching
IV CASCADED ARCHITECTURE AND PROCESSING DATA
This section introduces the constituent components of the cascaded architecture and the identification It also highlights the organization of the fingerprint database
A Components of the new system
The system is made of four linked components/modules as described in Fig 1:
i) Finger classification: Recognizing finger priority and classifying to basic patterns to match in the third and the fourth layer
ii) Feature extraction: Extracting minutiae, ridge-valleys structures to be used for matching in the next layers
iii) Affine matching: Performing minutiae matching by affine alignment as in Section 2.4
Trang 3iv) P-TPS matching: Performing P-TPS matching
according to the algorithm in [9]
Among the above components, it is necessary to present
more details about classification method in the first
component
Fingerprint classification
Forensic fingerprints and registered fingerprints in database
are classified into 10 types by combining the two methods of
Karu [4] and Wang [11] If this procedure produces
ambiguous outcomes, Karu’s method will be used Then, if
the outcomes are still ambiguous, basic ridge will be taken
into consideration If ridges at the core region are in good
quality, Wang’s method is applied [11] Otherwise, delta
points will be searched using Karu’s method [4] When the
core region and the delta points are unclear, we use basic
ridge to validate ambiguous cases This method helps
eliminate incomplete and unclear minutiae
Identifying basic ridge is quite straightforward by analyzing
curves which are represented in vectorized form By doing
this, the classification accuracy is increased up to 95%
Nevertheless, it is still insufficient to apply the cascaded
combination technique
In order to improve the reliability, in ambiguous cases,
fuzzy recognition technique type II will be employed If the
system cannot classify into a specific class, it will display a
list which is sorted in decreasing order of the classification
reliability By doing this, the classification layer always
provides the next layer with a controllable input
B Cascaded identification scheme
With the above components, the identification process is
performed sequentially as below:
Step 1: After the acquisition phase, latent fingerprint I q is
pre-processed and classified Ridge counts are calculated and
the finger order is determined As latent fingerprints normally
have a poor quality, it is difficult to extract minutiae
automatically Therefore, the fingerprint image will be
interactively edited by human experts to improve image
quality This is done by the assistance of a graphic tool Based
on this finger order, classified basic pattern and automatic
pre-extracted features of the template fingerprint It are then
retrieved from database for matching (in Step 4)
Step 2: In the classification module, basic pattern of I q is
determined either automatically or manually If there are
ambiguous outcomes, they are used for searching for the
match by the priority of the reliability Afterwards, I q is passed
to the feature extraction module
Step 3: In the feature extraction module [7], [14], the features
of I q including minutiae points and ridge-valley associated
structure with quality map are extracted by interactive editing
method The results are then passed to the affine matching
module (Step 4) and the P-TPS matching module (Step 5) If I q
has such poor quality that the system cannot perform minutiae
extraction on it, the process stops and delivers notification
Step 4: In the affine matching module, the features of
fingerprint I t in the database are sequentially retrieved to
match with the features of I q (taken from Step3) for
calculating the initial corresponding minutiae set and the
similarity S(I t ,I q ):
4.1 If S(I t ,I q ) < Smin then the two fingerprints are not
matched The system proceeds with the next fingerprint I t in the database
4.2 If S(I t ,I q ) >Smax the system appends I t to the output list Otherwise, that means S(It,Iq) ∈ [Smin, Smax], the system passes the corresponding minutiae pairs set and their associated ridge-valley pairs to P-TPS matching
Step 5: P-TPS matching Continue with Step 4 until all
features in the database that have the same finger code and pattern with those of Iq are matched
Step 6: Sorting the search result list according to priority:
finger code, basic pattern code, ridge count, similarity The system verifies based on the priority and displays the results
In the output list, corresponding minutiae pairs and associated ridge-valley pairs are displayed sequentially; each
pair of fingerprint image I q , I t is sorted in decreasing order of the similarity of sub-patterns (based on finger code, basic pattern, ridge count) on display screen There are many useful tools for assisting human experts in verifying the results With the identification process above, the first three steps could run in parallel However, that would not improve the running time efficiently Therefore, this paper only proposes parallelized matching method for Step 4 and Step 5 These are the two steps that account for the most time So they are implemented by a computer cluster with many nodes process
in parallel The cascaded architecture is described in Fig 1
Fig 1: The cascaded architecture scheme
C Organizing the database
In applications such as identity card and criminal card, each fingerprint is represented as one record having the following basic fields:
- Identity Card number;
- Personal information fields (full name, date of birth, sex, address);
- Finger code;
- Basic pattern;
- Left ridge count, middle ridge count, right ridge count, ridge density;
- Minutiae set;
- Fingerprint image (standard resolution 500 dpi, about 5 MB for a 10-finger set);
To accelerate the searching and matching processes, it is necessary to organize the fingerprint database in a sensible fashion In the database, fingerprints are indexed and organized hierarchically according to the following fields: fingerprint code, basic pattern, ridge counts and ridge density Information about minutiae points and associated ridge-valley
Trang 4pair is stored with the corresponding fingerprint image for
faster parallel searching
The extraction of basic attributes from fingerprint with
higher reliability for indexing remains an open research
problem [2] To reduce the false rejection rate (FRR), fuzzy
search method combining major code and minor codes for
coding ambiguous attributes has been proposed For example,
a ridge has whorl pattern with low ridge count can be
classified incorrectly into a loop ridge Hence, we need to
search both whorl ridge and loop ridge with major code
corresponds former searched whorl pattern and minor code
corresponds later searched loop pattern
D Parallel matching
For parallel matching solution, we setup a computer cluster
system with the following components:
i The server receives matching requests and performs
searching according to the basic attributes It then splits the
list into small pieces and distributes them to parallel
processing nodes for minutiae matching
ii Parallel processing nodes receive task and perform
matching They deliver results in terms of a search result list
to the server
iii Workstations receive the search results from server
They display the results to human experts who then perform
the final verification
V EXPERIMENTAL RESULTS
Our experiment compares the performance of the new
system with C@FRIS version 2009 A criminal identity
database of C@FRIS system applied at the Police Office of
Hanoi City contains 2.500.000 one-finger fingerprint cards
with the standard resolution of 500 dpi The hardware system
for performing the experiments consists of one mid-range
server, five PCs linked together by the star network topology
(parallelize with k=5) The performance of the system is
determined by the length of identification result list and the
search time In practice, the search time of C@FRIS system
and that of the new system when they use sequential
processing are equal, therefore we only compare the search
time when both systems are running in the parallel processing
mode
Sixty-four latent fingerprints with average or quite good
quality have been matched against the database
The result list is sorted by finger priority order, basic
pattern code order, ridge count order and the descending
similarity (in the same group) The length of the search result
list is the number of records on this list In practice, only
7-10% can be found in the database Hence, the length of the
real list is equal to the number of records on searched
fingerprint cards The remainder 90-93% unfound fingerprint
traces are from people without a criminal record In those
cases, examiners need to spend more time to verify until the
end of the list Thus, sorting the result list and determining the
maximum length of the real list for both found and unfound
cases play an important role
The experimental results show that the proposed method gains a high performance It increases search speed significantly In addition, it reduces on average 66.2% verification time per request compared with the case when the cascaded configuration is not applied
VI CONCLUSION
This paper proposes a cascaded architecture for latent fingerprint recognition system By applying combined fingerprint classification techniques, finger code recognition, ridge count, we were able to dramatically shorten the matching list Moreover, applying partial TPS matching method helps reduce the effect of non-linear distortion phenomenon
Thanks to the parallel matching process, search time has been significantly decreased The experimental results show that the new system gains higher performance and better, response time in large database
ACKNOWLEDGEMENT This work is partly supported by Vietnam National Foundation for Science and Technology Development
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