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2011 I

for Fing

Data & Software Engineering

Jl. G

Abstract Real time fingerprint identificatio

non-alphanumeric content matching shouldn

its speed factor. Real production-quality imp

equipped with specific machine architectu

factor. On this research, we focus on achiev

common machine with respect to fingerprint

We explore the possibility to optimize

fingerprint minutiae-based feature extrac

process. Hypothetically, in general using par

faster than using sequential process. Ta

multicore processor technology, optimization

fingerprint feature extraction is conducted us

by processor cores. We have identified

implement parallel process on 29 differen

executed once or more.

We compare result of average execution t

feature extraction using parallel and sequent

of Database 1 of Fingerprint Verification Co

2004. The experiment confirms the hyp

consistent result, i.e. faster about 57%, 55%

tested database.

Keywords extraction, feature, fingerpr

minutiae

I. INTRODUCTIONFingerprint feature extraction is one o

besides fingerprint matching, that estab

identification system. A fingerprint ide

recognizes an individual by searching th

template database for a match. It con

comparisons to establish if the individualdatabase and if so, returns the identifier

reference that matched. In an identificationestablishes a subjects identity (or determi

is not enrolled in the system database)

having to claim an identity [1].

Daily activities of a fingerprint ide

comprised of enrolment and identification

in Fig. 1. It demands real time result. So

optimize its speed (with respect to its accu

of its main stages up.

ternational Conference on Electrical Engi

17-19 July 2

arallel Processing

rprint Feature Extract

. Indrawan

1

, B. Sitohang

2

, S. Akbar

3

esearch Division, School of Electrical Engineering a

nesha No. 10 Bandung, West Java, Indonesia1gdindrawan@gmail.com

2benhard@stei.itb.ac.id3saiful@informatika.org

n based on original

t compromise with

lementation usually

e to optimize this

ng higher speed on

ccuracy factor.

execution time of

ion using parallel

allel process will be

ing advantage of

of execution time of

ing parallel process

the possibility to

routines that are

ime of a fingerprint

al process on Set B

test 2000, 2002, and

othesis and gives

, and 60% for each

int, identification,

f the main stages,

lish a fingerprint

ntification system

entire enrolment

ucts one-to-many

is present in theof the enrolment

system, the systemes that the subject

ithout the subject

ntification system

tself, as illustrated

it is important to

racy) by tuning all

Fig. 1 Fingerprint Identification Sys

Most of the works to o

identification system come fro

that raises classification/inde

optimize speed precedes th

important role during daily wosystem, especially on batch

enrolment or ten-print identific

accumulate time for their featur

II. FINGERPRINT FEOn this paper, we use Source

Vanak [2], for our parallel procfeature extraction. The algo

modular design, which provid

future enhancement and adaptat

Fig. 2 Fingerprint minutiae

neering and Informatics

11, Bandung, Indonesia

ion

d Informatics, ITB

tem: a) Enrolment; b) Identification

timize speed of fingerprint

m fingerprint matching stage

ing strategy. Our work to

t stage, which would play

rk of fingerprint identificationrocessing of many ten-print

tion since ten fingerprints will

e extraction.

TURE EXTRACTION

AFIS, open source AFIS by R.

essing analysing of fingerprintithm adopts MINDTCT [3]

s a framework for supporting

ion of the technology.

extraction modular design

J1 - 1

978-1-4577-0752-0/11/$26.00 2011 IEEE

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III.PARALLEL PROCESSING USING PROCESSOR CORESIn recent times, CPU clock speeds have stagnated and

manufacturers have shifted their focus on increasing core

counts. This is problematic because standard single-threaded

code will not automatically run faster as a result of those extra

cores. Leveraging multiple cores is easy for most server

applications, where each thread can independently handle a

separate client request, but is harder on the desktop -- becauseit typically requires that we take our computationally intensive

code and do the following [4]:

Partition it into small chunks. Execute those chunks in parallel via multithreading. Collate the results as they become available, in a thread-

safe and performance manner.

Although all above can be done with the classic

multithreading constructs, its awkward -- particularly the

steps of partitioning and collating. A further problem is that

the usual strategy of locking for thread safety causes a lot of

contention when many threads work on the same data at once.There are two strategies for partitioning work among

threads: data parallelism and task parallelism.When a set of tasks must be performed on many data values,

we can parallelize by having each thread perform the same set

of tasks on a subset of values. This is called data parallelism

because we are partitioning the data between threads. Incontrast, with task parallelism we partition the tasks; in other

words, we have each thread perform a different task.

In general, data parallelism is easier and scales better to

highly parallel hardware, because it reduces or eliminates

shared data (thereby reducing contention and thread-safety

issues). Also, data parallelism leverages the fact that there areoften more data values than discrete tasks, increasing the

parallelism potential.

Data parallelism is also conducive to structured parallelism,which means that parallel work units start and finish in the

same place in our program. In contrast, task parallelism tendsto be unstructured, meaning that parallel work units may start

and finish in places scattered across our program. Structured

parallelism is simpler and less error-prone and allows us to

farm the difficult job of partitioning and thread coordination

(and even result collation) out to libraries.

IV.PARALLEL PROCESSING FOR FINGERPRINT FEATUREEXTRACTION

Our parallel processing for fingerprint feature extraction

seems work mostly among data parallelism since many pixel

or pixel-block data can be partitioned and processedindependently. These kinds of data exist throughout main

work flow as described in Fig. 2.Fig. 3 gives more detail of main work flow of fingerprint

feature extraction. Implicitly, most of its stages consist of

work that potentially can be parallelized. Explicitly, two

appeared threads on the main work flow represent two parallel

works for detection of minutiae ending and bifurcation based

on fingerprints ridge and valley image. It related to minutiae

ending/bifurcation duality that will be explained on the next

part of this paper.

Fig. 3 Main work flow of fingerprint minutiae extraction

Table 1 gives overview of work using parallel processing

based on detail of main work flow of fingerprint feature

extraction given by Fig. 3.

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TABLEIOVERVIEW OF MAIN WORKS USING PARALLEL PROCESSING

No. Algorithm

1 File to memory transfer of image gray level info

2 Transformation of gray level info to ridge height info

3 Pixel-block mapping construction

4 Histogram analysing, smoothing, masking, and equalizing

5 Ridge-orientation detection and smoothing

6 Image binerization and smoothing7 Inner masking construction

8 Skeleton construction based-on ridge and valley image

Next more explanation will expose the usage of parallel

processing on fingerprint feature extraction.

A.Input Fingerprint FileThe simplest parallel processing applied at the beginning of

fingerprint feature extraction process, i.e. during transferfingerprint images gray-level information from file to the

memory. There is process for transferring such information

from 1-d array to 2-d array as illustrated by Fig. 4a.

Fig. 4 a) Threaded transfer of gray-level information of fingerprint image

(300x300 pixels) on memory; b) Threaded transformation of gray-level

information into fingerprint 3-D structure information [1]

Fig. 4b describes parallel processing routine for

transforming gray-level information into information that

represent fingerprint as a 3D structure with real ridges/hills

and valleys. For example, after transformation, pixel[x,y] with

gray-level equal to 0 will contain altitude/height of a hill, i.e.

255 means maximum height which is obvious ridge.

B. Generate Image MapsPixel-block structure constructed to locally analyze the

fingerprint, the image is divided into a grid of blocks. All the

pixels within a block are assigned the same results. Several

considerations must be made when using a block-based

approach. First, it must be determined how much local

information is required to reliably derive the desired

characteristic. This area is referred to as the window. The

characteristic measured within the window is then assigned to

each pixel in the block.

It is typically desirable to share data used to compute the

results assigned to neighboring blocks. This way some of the

image that contributed to one blocks results is included in the

neighboring blocks results as well. This helps minimize the

discontinuity in block values as we cross the boundary from

one block to its neighbor. This smoothing can be

implemented using a system where a block is smaller than its

surrounding window, and windows overlap from one block to

the next (Fig. 5a).

Fig. 5 Data block structure: a) its smoothing with overlapping windows; b) its

threaded block construction process

One additional consideration must be made when usingblocks. It must be determined how to handle the edges of the

image. The dimensions of the image will likely not be an even

multiple of blocks, and the windows surrounding blocks along

the perimeter of the image may extend off the image.

C.Binarize ImageThrough pixel-block scheme, histogram processing and

ridge orientation detection precede image binarization.The histogram of a digital image with gray levels in the

range [0,L - 1] is a discrete function h(rk) = nk, where rk is the

kth gray level and nk is the number of pixels in the image

having gray level rk. Local histogram per block was extracted

as illustrated in Fig 6a.

Smoothing local histogram for each block was conducted

by adding its histograms pixels number of each gray level,

with correspondence value from histogram of neighbor blocks

(Fig 6b).

Fig. 6 a) Threaded local histogram process; b) Threaded smoothing local

histogram with respect to its neighbour blocks

Histograms are the basis for numerous spatial domain

processing techniques. Histogram manipulation can be used

effectively for the next image segmentation and enhancement

process.

Fig. 7 shows masking process for segmentation.The termsegmentation is generally used to denote the separation of

fingerprint area (foreground) from the image background.

Separating the background is useful to avoid featuresextraction in noisy areas that is often the background.

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Fig. 7 The whole masking process

Masking process starts by clipping contrast where some

upper/lower area of brightness histogram is not included in

contrast calculations to eliminate some noise (Fig. 8).

Fig. 8 Threaded local contrast clipping process

An image with low contrast has a histogram that will be

narrow and will be centered toward the middle of the gray

scale [5]. Otherwise, high-contrast image cover a broad range

of the gray scale and the distribution of pixels is not too far

from uniform, with very few vertical lines being much higher

than the others.

Next, to separate fingerprint from background, absolute

minimum contrast detection is used based on certain expected

area width as shown by Fig. 8. There is also relative contrast

detection based on ratio of local and global absolute contrast

(Fig. 9).

Fig. 9 Threaded process to detect minimum contrast: a) absolute; b) relative

Beside above methods on histogram contrast manipulation,

the next pixel level processes intended to kill some errors incontrast detection by majority vote (Fig. 10).

Fig. 10 Threaded process to kill errors in contrast detection by majority vote

Image enhancement process is histogram equalization, i.e. a

method in image processing of contrast adjustment using the

image's histogram. This method usually increases the global

contrast of many images, especially images with close

contrast values. Through this adjustment, the intensities can bebetter distributed on the histogram. This allows for areas of

lower local contrast to gain a higher contrast. Histogram

equalization accomplishes this by effectively spreading out

the most frequent intensity values (Fig. 11).

Fig. 11 Threaded local histogram equalization

After image enhancement, orientation detection routine will

computes local ridge orientation (Fig. 12) using its local

histogram. The presence of a significant peak in a histogramdenotes an oriented pattern, whereas a flat or near-flat

histogram is characteristic of an isotropic signal. A white

block in the background indicates no local ridge orientation.

Orientation array will contain byte-encoded ridge

orientation angle for every block in the image. The local ridge

orientation at a pixel [x,y] is the angle xy that the fingerprint

ridges, crossing through an arbitrary small neighborhoodcentered at [x, y], form with the horizontal axis. Orientation

field is in rather strange units. Normally angle is 0 ... 2.

Orientation however doesn't care whether it is up or down, so

it is only 0 ... . In order to use full 0 ... 2 interval, we alwaysmultiply the orientation angle by two. This creates strange

angle, but it is very practical to have orientation in 0 ... 2

range. We use it to convert the orientation angle to vector.

Vector representation of angles is useful, because vectors are

easy to add up and average, especially when each vector has

different weight.

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Instead of computing local ridge orientation at each pixel,

we estimate the local ridge orientation at discrete positions(this reduces computational efforts and still allows estimates

at other pixels to be obtained through interpolation). The

fingerprint orientation image (also called directional image), is

a matrix whose elements encode the local orientation of the

fingerprint ridges. Each element ij, corresponding to the node

[i, j] of a square-meshed grid located over the pixel [xi, yj],

denotes the average orientation of the fingerprint ridges in aneighborhood of [xi, yj]. An additional value rij is often

associated with each element ij to denote the reliability (or

consistency) of the orientation. The value rij is low for noisy

and seriously corrupted regions and high for good quality

regions in the fingerprint image.

Fig. 12 Threaded ridge orientation detection

Along with orientation detection result, ridge equalization

takes equalized image and performs smoothing on every pixel,then returns the smoothed image (Fig. 13). Smoothing is not

the same everywhere. Smoothing is always performed along a

line parallel to ridge orientation in the block that contains the

smoothed pixel. This has the effect of making ridge structure

easier to see for the algorithm.

Fig. 13 Threaded ridge equalization from equalized image

Based on previous process, now we want to binarize the

image (Fig. 14). The simplest approach uses a global

threshold tand works by setting the pixels whose gray-level is

lower than tto 0 and the remaining pixels to 1. But here, we

cannot binarize using such simple threshold (e.g. 50%brightness), because brightness levels vary in parts of the

image. We need some measure of local brightness produced

by "orthogonal" image processing. Local brightness is

computed as average intensity of pixels lying along a line

orthogonal to local ridge orientation (effectively lying on

ridge cross-section). This average is in fact what smoothing

algorithm computes, so we just reuse the smoothing algorithmfrom previous step, but this time we give it angle offset that

makes the smoothing algorithm work along lines orthogonal

to local ridge orientation.

Fig. 14 Binarization process

Additional algorithms merges binary smoothing output into

binary image. The process basically looks for an odd 1 in

neighborhood of 0s or for an odd 0 in neighborhood of 1s.

Also there is algorithm to remove unwanted checkerboard-like

situations in binary image. Checkerboard-like pixel quad will

create one ridge and one valley crossing each other diagonally

when thinned.

Inner masking process outputs part of binary image area

(inside border in Fig. 15) that will be processed by the nextfingerprint skeleton construction.

Fig. 15 Threaded inner masking process

D.Detect MinutiaeFingerprint skeleton construction takes two threads for

ridge- and valley- based minutiae extraction (Fig. 3) because

of ridge ending/bifurcation duality. Fig. 16a shows a portion

of the fingerprint binary image where the ridge lines appear as

dark traces on a light background with its two ridge endings (1,

2) and one bifurcation (3). At the other side, Fig. 16b shows

on its negative image, the corresponding minutiae take the

same positions, but their type is exchanged: ridge endings now

appear as bifurcations and vice versa. This property is known

as ridge-ending/bifurcation duality.

Fig. 16 The ridge ending/bifurcation duality in a) a binary image and b) its

negative image (i.e., dark and bright pixels are swapped).

Inside fingerprint skeleton construction many process

happened. There is algorithm to thin ridges in binary image

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that basically removes border pixels (detec

binary operations on pixels) in a loop untilis left. Also there is constructed helper

fingerprint as a set of minutiae connecte

ridges which is called skeleton. Each ridge

list of pixels. Ridge tracer algorithm

minutiae as pixels with 3 or more neighbor

pixels that connect minutiae and also

minutiae.

E.Remove False MinutiaeFor accurate fingerprint skeleton cons

important algorithms to remove false/spur

islands and lakes, holes, hooks, and overlap

Islands and lakes are somewhat larger th

in the friction skin and they are often

therefore, they typically will have a pair opoints detected at opposite ends. A hole is

an island or lake, only smaller, and the lo

one minutia point on it. A hook is a s

protrudes off the side of a ridge or v

typically has two minutiae of opposite typiece of ridge and the other in a smalrelatively close to each other. An overlap i

a ridge or valley. A break in a ridge ca

endings to be detected, while a break in a v

bifurcations.

Fig. 17 Various false minutiae a) island; b) lake; c) h

F.

Output Minutiae InformationFinally, minutiae collection algorithm fi

previously constructed fingerprint skeleton

no parallel processing here. For the

extraction process, we give small portion he

Fig. 18 Minutiae collection proc

V. EXPERIMENT RESULTWe compare result of average exe

fingerprint feature extraction using paral

process on machine with processor AMDGHz and RAM 4 GB. As tested database

Database 1 (collected by using small-size a

sensors) of Fingerprint Verification Conte

2002 [7], and 2004 [8]. Each database con

images.

ted through simple

only one-pixel lineclass that models

by a network of

in the skeleton is a

recognizes initial

s then iterates over

merges adjacent

ruction, there areous minutiae, like

(Fig. 17).

n the size of pores

lliptical in shape;

candidate minutiaefined similarly to

op need only have

pike or spur that

lley. This feature

e, one on a smalll valley, that area discontinuity in

uses 2 false ridge

lley causes 2 false

le; d) hook; e) overlap

ds all minutiae in. Actually there is

ake of complete

re.

ss

ution time of a

lel and sequential

riple Core @ 2.20, we use Set B of

d low-cost optical

t (FVC) 2000 [6],

ains 80 fingerprint

Fig. 19 Comparison of Parallel vs. Se

The experiment result (Fi

execution time per fingerprint

extraction algorithm usingsequential process. It gives con

result -- through three attempts

500 ms, and 800 ms difference

2000, FVC 2002, and FVC 200

VI.CONCNot all implemented parall

paper because they exist on s

algorithm. On exposed algor

parallel process really useful t

or pixel level data processing.

This paper highlights utilizafingerprint feature extraction b

parallel process for fingerprint

than using sequential process o

The experiment result confi

consistent result, i.e. faster abo

tested database above.

ACKNOWL

This research has been supp

Technology (ITB) under Insti

Program 2010 with Contract

ITB/SPK/2010. We wish to th

discussion to make this paper a

REFER

[1] D. Maltoni, D. Maio, A. K.Fingerprint Recognition, 2nd ed,

[2] R. Vanak. (2009) SourceAFISAvailable: http://sourceforge.net/

[3] C. I. Watson, et all, User's Gui(NBIS), National Institute of Sta

USA, 2001.

[4] J. Albahari. (2011) Parallel ProgAvailable: http://www.albahari.c

[5] R. C. Gonzalez, R. E. Woods,Jersey: Prentice Hall, 2002

[6] D. Maio, et all,FVC2000: FingeTransactions on Pattern Analysi

pp. 402412, 2002.[7] D. Maio, et all, FVC2002

Competition, Proc. 16th Int'l C

811-814, Aug. 2002.[8] D. Maio, et all,FVC2004: Third

Proc. Int'l Conf. Biometric Authe

quential Process of Extraction Stage

. 19) shows faster average

roduced by fingerprint feature

arallel process rather thansistent average execution time

-- and produce about 400 ms,

on tested Database 1 of FVC

, respectively.

LUSIONS

el processes exposed on this

upporting algorithm for main

ithms, it clearly shows that

speed up heavily pixel block

tion of parallel processing forased on hypothesis that using

eature extraction will be faster

multicore processor.

rms the hypothesis and gives

t 57%, 55%, and 60% for each

EDGMENT

orted by Bandung Institute of

tution Research Improvement

Number 1112/K01.16/INST-

ank to R. Vanak [2] for great

complished.

NCES

Jain, S. Prabhakar, Handbook of

New York: Springer Verlag, 2009.

homepage on SourceForge. [Online].

projects/sourceafis/

e to NIST Biometric Image Softwarendards and Technology, Gaithersburg,

amming on Threading in C#. [Online].om/threading/part5.aspx

igital Image Processing, 2nd ed, New

rprint Verification Competition, IEEE

Machine Intelligence, vol. 24, no. 3,

: Second Fingerprint Verification

onf. Pattern Recognition, vol. 3, pp.

Fingerprint Verification Competition,

ntication, pp. 1-7, July 2004.