Review of Sequential Access Method for Fingerprint Identification

Abstract

Real time fingerprint identification is usually equipped with specific computation machine architecture to optimize speed factor. Focusing on achieving better speed performance of fingerprint identification on common computation machine, a disquisition was conducted on sequential access method for fingerprint identification, with its underlying data structure designed to work with parallel processing. Hypothetically, parallel processing based on multi-cores processor technology, can give faster result without reducing accuracy. If multi core processor was detected, simultaneous processes would run on fingerprint matching-pairs to find its similarity score, respectively. Experiment confirms that speed performance of fingerprint identification using sequential access method with parallel processing outperforms the one without parallel processing. For both strategy, even though using parallel processing confirms faster result, experiment shows that searching time O(n) still linearly depends on number of fingerprints in database. Avoiding such searching time trend, hypothetically, need strategy of direct access method utilization.

Full text

TELKOMNIKA, Vol.10, No.2, June 2012, pp. 335~342
e-ISSN: 2087-278X (p-ISSN: 1693-6930)
accredited by DGHE (DIKTI), Decree No: 51/Dikti/Kep/2010  335
Received January 21, 2012; Revised April 30, 2012; Accepted May 7, 2012
Review of Sequential Access Method for Fingerprint
Identification
G. Indrawan*, B. Sitohang, S. Akbar
Data & Software Engineering Research Division
School of Electrical Engineering and Informatics, Bandung Institute of Technology
Jl. Ganesha No. 10 Bandung, West Java, Indonesia, Ph. +6222 2502260, Fax +6222 2534222
e-mail: gdindrawan@gmail.com*, benhard@stei.itb.ac.id, saiful@informatika.org
Abstrak
Identifikasi sidik jari waktu-nyata pada umumnya menggunakan arsitektur mesin komputasi
spesifik untuk mengoptimalkan faktor kecepatan. Berfokus pada kinerja kecepatan yang lebih baik dari
identifikasi sidik jari pada mesin komputasi umum, penyelidikan dilakukan pada metode akses sekuensial
identifikasi sidik jari, dengan struktur data yang dirancang untuk bekerja dengan pemrosesan paralel.
Berdasarkan hipotesis, pemrosesan paralel berbasis teknologi prosesor multi-core, mampu memberikan
hasil lebih cepat tanpa mengurangi akurasi. Jika prosesor multi-core terdeteksi, beberapa proses akan
berjalan simultan pada beberapa pasangan pencocokan sidik jari untuk menghasilkan nilai kesamaannya
masing-masing. Eksperimen mengkonfirmasi kinerja kecepatan identifikasi sidik jari menggunakan metode
akses sekuensial dengan pemrosesan paralel lebih baik dibandingkan tanpa pemrosesan paralel. Untuk
kedua strategi, meskipun menggunakan pemrosesan paralel mengkonfirmasi hasil yang lebih cepat,
eksperimen menunjukkan waktu pencarian O(n) masih bergantung secara linier pada jumlah sidik jari
dalam database. Menghindari tren waktu pencarian tersebut, berdasarkan hipotetis, memerlukan strategi
pemanfaatan metode akses langsung.
Kata kunci: akurasi, identifikasi, kecepatan, pencocokan, sidik jari
Abstract
Real time fingerprint identification is usually equipped with specific computation machine
architecture to optimize speed factor. Focusing on achieving better speed performance of fingerprint
identification on common computation machine, a disquisition was conducted on sequential access
method for fingerprint identification, with its underlying data structure designed to work with parallel
processing. Hypothetically, parallel processing based on multi-cores processor technology, can give faster
result without reducing accuracy. If multi core processor was detected, simultaneous processes would run
on fingerprint matching-pairs to find its similarity score, respectively. Experiment confirms that speed
performance of fingerprint identification using sequential access method with parallel processing
outperforms the one without parallel processing. For both strategy, even though using parallel processing
confirms faster result, experiment shows that searching time O(n) still linearly depends on number of
fingerprints in database. Avoiding such searching time trend, hypothetically, need strategy of direct access
method utilization.
Keywords: accuracy, fingerprint, identification, matching, speed.
Copyright © 2012 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
A fingerprint identification system recognizes an individual by searching the entire
enrolment templates in database for a match. It conducts one-to-many comparison/matching to
establish if the individual is present in the database and if so, returns the identifier of the
enrolment reference that matched. In an identification system, the system establishes a
subject’s identity (or determines that the subject is not enrolled in the system database) without
the subject having to claim an identity [1]. This general concept applied to others biometric
identification system, like palm print [2].
Figure 1 shows daily activities of a fingerprint identification system that consists of two
main stages, i.e. enrolment and matching. On large database (could be several million
fingerprints), identification demands real time result, so it is important to optimize its speed with
respect to its accuracy by tuning up all of those main stages. Previously, we have

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reportedspeed optimization of fingerprint feature extraction in [3], which play important role
during ten-print batch processing of enrolment andmatching.
Figure 1. Fingerprint identification system: a) enrolment; b) matching
The identification process can be speeded up by reducing the number of comparisons
that are required to be performed. Sometimes, information about sex, race, age, and other data
related to the individual are available and the portion of the database to be searched can be
significantly reduced; however, this information is not always accessible (e.g., criminal
identification based on latent fingerprints) and, in a general case, intrinsic information of the
fingerprint samples has to be used for an efficient retrieval.Searching involves lookup or
indexing operations (finding the value associated with a key) using index. Prudent use of index
can make searching faster by eliminating the need to sort (almost always the ultimate goal of
sorting is to organize a search) and thus reducing I/O cost [4]. Searching involves sequential or
direct access of data. Sequential access is the concept of accessing (or reading) records from a
table in sequential order, i.e. from the top to bottom, one after another. Direct access is the
concept of accessing (or reading) specific records from a table in no particular order by
specifying which row(s) to be read. The row(s) value possibly comes from processing of specific
data representation, like quad-tree, kd-tree, and range-tree [5].
Although hypothetically, direct access of fingerprint identification method would
outperforms its sequential access method, it is however still important to know some aspects of
sequential access method as a base for improvement. Sequential access method for finger print
identificationis the most natural process of fingerprint identification thatbasically conducts one-
to-many matching between input fingerprintand enrolled fingerprints in the database.Because of
method’s simplicity, method’s most natural process, and method’s strong foundation for
improvement, a disquisition of this method was taking place based on [6], [7] and using [8] as a
basic framework. While [6] gives translation- and rotation- variant features (i.e. minutiae’s
absolute coordinate and absolute orientation), [7] gives translation-invariant and rotation-variant
features in star configuration (i.e. center-minutiae-to-neighbor-minutiae edge length, and center-
minutiae’s absolute orientation), [8] gives translation- and rotation- invariant features in star
configuration (i.e. center-minutiae-to-neighbor-minutiae edge length, center-minutiae’s relative
orientation, and neighbor-minutiae’s relative orientation), this paper observed parallel
processing design that was implemented on [8] to gives improvement on fingerprint
identification speed without reducing its accuracy.
2. Research Method
Sequential access method for fingerprint identification uses fingerprint’s local features
(minutiae-based only) without pre-selection stage (clasification process to produce pre-defined
classes). Research design on this method involves:
a. Global data structure used by enrolment stage (Figure 2).
b. Sequential access method run above designated global data structure (Figure 3).
c. The algorithm and its underlying main object (Figure 4).
d. Derivative featurescomputation and comparison(Figure 5).
e. Similarity score mechanism from pairing traversal process (Figure 6).
f. Monte-Carlo-based experiment at Result and Discussion (Figure 7, Figure 8, Table 1).
2.1. Global Data Structure at Enrolment Stage
Enrolment uses main Abstract Data Type (ADT) called Person which is primarily a way
to group multiple fingerprints belonging to one person. Person consists of Fingerprint object that

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contains basic information about the fingerprint, i.e. Image that is used to perform template
extraction and Template that is used for identification. Image is in raw image format that must
be set before generating valid Template. The format of this image is a simple raw 2D array of
bytes. Every byte represents shade of gray from black (0) to white (255). Template is an
abstract model of the fingerprint and once was generated, Image property can be set to null to
save space. Figure 2 shows memory space requirement for template contains fingerprint
intrinsicinformation. Templates are better than fingerprint images, because they require less
space and they are easier to match than images.
Person object is designed to be easy to serialize in order to be stored in binary-format
(BLOB) under Person attributein database.This binary-format attribute indexed with numeric-
format ID attribute (Figure 2). Here, we need to determine what key-like information can be used
to construct data structure for searching. Additional requirement, this information should be
translation- and rotation- invariant for reliability factor related to identification accuracy. Global
structure data at Figure 2 do not contains translation- and rotation- invariant information
(consider as primitive feature), but that information can be used to generate translation- and
rotation- invariant key-like information (consider as derivative feature).
Figure 2. Global data structure of enrolment
Figure 3. Sequential access method for
identification: a) basic approach;
b) improvement approach
2.2. Sequential Access Method at Identification Stage
Figure 3a shows basic approach of sequential access method for fingerprint
identification. The matching is a kind of associative array with similarity measure. Probe
fingerprint on input is compared sequentially with database’s candidate fingerprints stored in the
associative array, candidates are sorted by result of the similarity measure function, and the
closest match is returned. Similarity measure function (matching algorithm) computes similarity
score that represents degree of similarity between two templates.
Enhancement of basic approach (Figure 3b) utilizes p cores of processor so there will
be p concurrent tasks (parallel process) on p fingerprint matching-pairs to produce psimilarity
scores, respectively. A fingerprint matching-pair consists of probe fingerprint and a candidate
fingerprint from database to be match with.
2.3. The Algorithm
Identification stage at Figure 3 uses algorithm (Figure 4) that works integrated with its
main Abstract Data Type called Matcher. Matcher consists of several objects, i.e. ProbeIndex

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and EdgeTable to store derivative features belong to probe and candidate fingerprint,
respectively. The others objects, i.e. MinutiaPairing, EdgeLookup, and PairSelector work
together during edge pairing process to produce pair of the longest paths belong to probe and
candidate fingerprint (Figure 6a). Traversal of candidate’s longest path will give best similarity
score as indicator for best similarity to the probe fingerprint. During traversal, some parameters
values saved by MinutiaPairing, similar edges belong to probe and candidate saved by
EdgeLookup, and decision for next minutiae pair to be travelled handled by PairSelector.
Figure 4. Algorithm of sequential access method for fingerprint identification
Several things are important to note regarding this algorithm:
a. Small size translation-androtation-variant minutia information (primitive features) are
persisted in database, i.e. its location (x,y) and orientation t, as shown by Figure 2.
b. Relative big size translation-androtation-invariant minutia information (derivative features)
from primitive features are compute in dynamic run-time memory for identification, i.e. its
length of minutiae-edge d, and relative angle between minutiae – minutiae-edge (β
1
and β
2
),
as shown by Figure 5b.
c. Derivative features are constructed at EdgeTablebelong to Matcher object. One table for the
probe fingerprint (encapsulated by ProbeIndex) and one table for candidate fingerprint to be
matched against.
d. There will be global computation time O(n) for identification where nis number of candidate
fingerprints in database. For each matching between a probe and candidate fingerprint, there
will be local computation time O(i*j) where i is number of records of probe’s minutia
information (primitive features) and j is number of records of candidate’s primitive features,
as shown by Figure 2.
e. The output will be pairing of the longest paths which are similar between probe path and its
counterpart candidate path,as shown by pair of green paths at Figure 6a. These longest
paths have its own root (consists of pair of minutiae index, one from probe and one from
candidate), shown by gray record at Figure 2, as final objective of fingerprint identification.
Traversal of the longest candidate path, start from its root, accumulate a similarity score
(from computation of some parameters taken during traversal).
2.4. Derivative Features Computation
Figure 5b illustrates computation of translation-androtation- invariant minutia information
(derivative features) from primitive features. Two minutiae connected by line construct edge.
From left edge of Figure 5b, first minutia k is in the upper right and is depicted by the dot

TELKOMNIKA e-ISSN: 2087-278X 
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representing location (x
k
,y
k
) and the arrowed line pointing down representing orientation t
k
. A
second minutia j is in the lower left with orientation pointing down and to the left.
To account for relative translational position, the distance d
kj
is computed between the
two minutia locations. This derivative feature will remain relatively constant between
corresponding points on two different finger impressions regardless of how much translation and
rotating may exist. Additional derivative featuresis angle between each minutia’s orientation and
the intervening line between both minutiae. This way, these angles remain relatively constant to
the intervening line regardless of how much the fingerprint is rotated. In Figure5b, the angle θ
kj
of the intervening line between minutia k and j is computed by taking the arctangent of the slope
of the intervening line. Angles β
k
and β
j
are computed relative to the intervening line by
incorporating θ
kj
and each minutia’s orientation t.
For each pair-wise minutia comparison, an entry is made into an edge table as shown
by Figure 5c, i.e. consists of { d
kj
, β
1
, β
2
}, where β
1
= max (β
k
, β
j
) and β
2
= min (β
k
, β
j
). So that
in left edge illustration as shown by Figure 6b, β
1
= β
k
and β
2
= β
j
. Entries are stored in the Edge
Table belong to Matcher object. In ascending order of distance, the table is trimmed at the point
in which a maximum distance and a maximum neighbour threshold are reached. Making these
measurements between pairs of minutiae, an edge table must be constructed for each probe
fingerprint and every candidate fingerprint we wish to match with.
Figure 5. Intrinsic information from portion of
the same fingerprint with slight different
impressions:
a) left: probe, right: candidate; b)
derived translation- and rotation- invariant
information of minutiae points; c) sample of
fingerprint key-like information in tabular form
where yellow records related to displayed entries
at a) and black records indicate similar entries
Figure 6. Pairing traversal:
a) the longer the green
path, the higher the similarityscore; b) pairing
traversal information in tabular form
2.5. Derivative Features Comparison
Derivative feature comparison takes the minutia edge tables from two separate
fingerprints and look for similar entries between the two tables. Figure5a shows part of two
impressions of the same fingerprint with slight differences in both translation and rotation. The
left print represents a probe impressionin which all its minutiae have been pair-wised computed
with relative measurements stored in edge table P with sample (Figure 5c).
The relative measurements computed from the particular pair of minutia (Figure 5b)
have been stored as the m
th
entry in table P, denoted P
m
. The notation of individual values
stored in the table are represented as lookup functions on a given table entry. For example, the
index of the upper right minutia is stored in table entry P
m
and is referenced as k(P
m
), while the

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distance between the two minutiae is also stored in table entry P
m
and is referenced as d(P
m
).
The right print represents a candidate impressionfrom database, and uses similar notation,
except that all its pair-wise minutia comparisons have been stored in table G, and the
measurements made on the two corresponding minutia in the candidate print have been stored
in table entry G
n
.
Black records at Figure 5c indicate similar entries. Three tests are conducted to
determine if table entries P
m
and G
n
are similar. The first test checks to see if the corresponding
distances are within a specified tolerance:∆
d
(d(P
m
), d(G
n
) ) <T
d
.The last two tests check to see
if the relative minutia angles are within a specified tolerance:∆
β
(β
1
(P
m
), β
1
(G
n
)) <T
β
, and ∆
β
(β
2
(P
m
), β
2
(G
n
)) <T
β
.
Figure 6 shows part of process to calculate similarity score of a candidate fingerprint
compared to the probe fingerprint. Maximum similarityscore produced by
accumulatingsimilarityscore parameters during traversal of the longest path (maybe
discontinuous path) of candidate fingerprint. Pair of green paths at Figure6a actually is not pair
of the longest paths but it illustrates the process to find it at probe and candidate fingerprint.
These longest paths have its own root -- consists of probe’s minutia index and candidate’s
minutia index, shown by gray record at Figure2, as final objective of fingerprint matching.
Figure6b shows sample of pairing traversal information start from minutia pair (0,6) until
minutia pair (25, 5). Yellow records show displayed edge at Figure6a (not all entries at edge
table Figure6b shown at Figure 6a). Green, red, and blue records are similar edge pair (each
from probe’s edge and candidate’s edge) belong to its minutia pair process during traversal.
Green records also special entries that constructs pair of the longest path, each longest path for
probe and candidate fingerprint.
2.5. Similarity Score
Similarity score between probe fingerprint and one candidate fingerprint from database,
is obtained through traversal of pairing of the longest paths, each path belongs to the probe and
candidate fingerprint to be match with (Figure 6). The longer the pair of these longest paths, the
higher the similarity between those both fingerprints.
There are two conducted comparisons related to the similarity score, i.e.:
a. Finding pairing of the longest path through comparison with others pairing of the longest
path found during iteration on probe and one candidate fingerprint.
b. After finding pairing of the longest path with the best similarity score (represent the best
similarity between probe and one candidate fingerprint),that score put in unsorted
associative array with Person ID as a key, and Similarity (Match) Score as the value
(Figure3). Person ID represents index (location) of a candidate fingerprint in database and
Match Score indicate candidate’s best similarity score obtained during pairing traversal with
probe fingerprint. The highest score in associative array become the strong similar
candidate among others candidates fingerprint in database.
Some parameters for traversal score computation of the pairing of the longest path are
minutiae pair count, correct type minutiae pair count, and supported minutiae pair count. The
last two parameters are used for strict filtering of minutiae pair count forbetter deal with false
matches that cause problems with low quality fingerprints.
3. Results and Discussion
Monte Carlo analysis was conducted on experiment, that rely on repeated random
sampling to compute the results. The analysis is most suited for calculation by a computer and
tends to be used when it is infeasible to compute an exact result with a deterministic algorithm
[9].The analysis follows this procedure: 1) define a domain of possible inputs; 2) generate inputs
randomly from a probability distribution over the domain; 3) perform a deterministic computation
on the inputs; 4) aggregate the results.
In this procedure the domain of inputs is set A of database 1 (fingerprint image collected
by using small-size and low-cost optical sensors) of fingerprint verification contest (FVC) 2000
[10], 2002 [11], and 2004 [12], each with 300x300, 388x374, and 640x480 pixel size. Then each
database was set up with 100, 400, and 800 data population (every set A of FVC database has
maximum value of 800 data population). So we have domain of inputs come from nine different
databases (differ from FVC year and number of data population).

TELKOMNIKA
Review of
Sequential Access Method
Experiment was conducted f
of random inputs
(a hundred should be enough) per database
each input
(searching/matching itself on database)
our final result per database
, as shown by
time (Avg), sample s
tandard
matching time (Max).
Sample standard d
eviation
below(the use of N
− 1 instead of
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Carlo-based experiment on
(single core)
and with parallelprocessing
RAM 4 GB and Intel® Core
™i5
Figure 7.
Monte Carlo analysis for s
accessed method
with single core and
FVC 2000
database
Figure 7 shows detail of
fingerprint identification without and with parallel processing on FVC 2000 database
approximation of Avg, s
ample
Complete results for other databases (FVC 2002
8clearly shows that for all nine databases,
e-ISSN: 2087-278X
Sequential Access Method
for Fingerprint
Identification
Experiment was conducted f
or each of nine data bases.
We generate
(a hundred should be enough) per database
then perform a computation on
(searching/matching itself on database)
. Finally, we aggregate the results to obtain
, as shown by
Table 1, i.e.,
the approximation of
tandard
deviation (σ), minimum matching time (Mi
n
eviation
was calculated using formula with
Bessel's correction
− 1 instead of
N):
̅

ଶ
the observed values
(matching time)
of the sample items and
of these observations.
To get an accurate approximation for
this procedure has two other common properties of Monte Carlo
method
. First, the inputs
truly be random. Second, there shou
ld be a large number of inputs.
We conducted
sequential access method algorithm without
parallel processing
and with parallelprocessing
(multi-cores) on
Microsoft® Windows™ 7
™i5
-2430M (Quad Cores)@ 2.20 GHz.
Monte Carlo analysis for s
equential
with single core and
multi-coreson
database
Figure 8.
Speed performance
comparison of s
equential accessed
method
with single core and
of nine databases
Table 1. Parameters
approximation
sequential accessed method with single
core and multi-cores
of nine databases
Figure 7 shows detail of
Monte Carlo analysis for
sequential accessed method of
fingerprint identification without and with parallel processing on FVC 2000 database
ample
standard deviation range (from σ- to σ+
),
Complete results for other databases (FVC 2002
and FVC 2004) were shown on Table 1.
8clearly shows that for all nine databases,
sequential accessed method of fingerprint

Identification
(G. Indrawan)
341
We generate
arbitrary number
then perform a computation on
. Finally, we aggregate the results to obtain
the approximation of
average matching
n
), and maximum
Bessel's correction
[13]
(1)
of the sample items and
̅ is the
To get an accurate approximation for
those all values,
. First, the inputs
should
We conducted
this Monte-
parallel processing
Microsoft® Windows™ 7
machine with
Speed performance
equential accessed
with single core and
multi-cores
of nine databases
approximation
of
sequential accessed method with single
of nine databases
sequential accessed method of
fingerprint identification without and with parallel processing on FVC 2000 database
. It shows
),
Min, and Max.
and FVC 2004) were shown on Table 1.
Figure
sequential accessed method of fingerprint

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identification with parallel processing outperforms the one without parallel processing. It also
shows that even though using parallel processing gives faster result, in general searching time
O(n) is still dependsheavily on number of n fingerprints in database. Without parallel processing,
searching time O(n) is almost linearly depends on number of n fingerprints in three different
tested database (FVC 2000, FVC 2002, and FVC 2004), while using parallel processing by
quad core processor, searching time O(n) is in sub linear trend (o(n)) where time increasing is
only about one-third to a half of searching time O(n) without parallel processing.
It can be analyzed the cause of the experiment result above (that also two main
disadvantages of sequential access method) which are lied on its two big sequential accesses
conducted during identification process, i.e.:
a. Sequential access to the whole n candidate fingerprints in database during matching probe
fingerprint. Even though highest similarity score has already obtained from candidate
fingerprint at very first location of database, the algorithm must still examine the rest of
candidate fingerprints in database.
b. Sequential access to the whole minutia pairs [i,j] during matching probe to each candidate
in database, where i is number of probe’s minutia and i is number of candidate’s minutia.
Even though pair of the longest path (Figure 7a) has already obtained from the very first
minutia pairs [0,0], the algorithm must still examine the rest of minutia pairs [i,j].
4. Conclusion
We have observed sequential access method forfingerprint identification without and
with parallel processing. Theexperiment gives empiric resultsthat in general searching time O(n)
still dependsheavily on number of n fingerprint in database, i.e. in linear and sub linear fashion,
respectively.With this result, we can predict what would happened if database contains millions
number of fingerprints. The algorithm becomes unreliable with long time response without using
parallel processing, and with parallel processing there must be some specific hardware
implementation that should be avoided without doing algorithm optimization first.To overcome
such kind of disadvantages of sequential access method for fingerprint identification, future work
on designing and implementing of direct access method with various efficient representation of
data structure, need to be observed.
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