Faculty of Automation and Computer Science

Eng. Radu-Florin Miron

PhD THESIS

Distributed Fingerprint Identification System

ABSTRACT

Scientific Coordinator,

PhD. Prof. Eng. Tiberiu Ștefan Leția

PhD Thesis – Distributed Fingerprint Identification System

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Table of Contents

Chapter 1. Introduction

1.1 The biometry

1.2 The history of fingerprint recognition

1.3 General aspects of biometric systems

1.4 Thesis objectives

1.5 Thesis content

Chapter 2. Conceptual models for fingerprint recognition

2.1 The steps required for the recognition process

2.2 Fingerprint image acquisition

2.3 Feature extraction

2.4 Image segmentation

2.5 Local ridge orientation and frequency

2.6 Singularity detection

2.7 Image enhancement

2.8 Image binarization and thinning

2.9 Minutiae detection

2.10 False minutiae rejection

2.11 Fingerprint matching

2.12 Experimental results

2.13 Conclusions

Chapter 3. Fuzzy Logic Method for Partial Fingerprint Recognition

3.1 Introduction

3.2 Software architecture

3.3 Method presentation

3.4 Conclusions

Chapter 4. The design of a distributed fingerprint identification system

4.1 Introduction

4.2 State of the art

4.3 Requirements and specifications

4.4 Use cases

4.5 General hardware architecture

4.6 Communication protocol

4.7 Hardware configuration of the servers

4.8 Server application design

4.9 Client application design

4.10 Mixt node application design

4.11 Conclusions

PhD Thesis – Distributed Fingerprint Identification System

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Chapter 5. Implementation Aspects for the Proposed System

5.1 Server implementation

5.2 Fixed client implementation

5.3 Mobile client implementation

5.4 Mixt node implementation

5.5 Experimental results

5.6 Conclusions

Chapter 6. Extension Modules for the Proposed System

6.1 Security module

6.2 Mobile terminal tracking using GPS

6.3 Secured communication system and method between fixed and mobile terminals

6.4 Conclusions

Chapter 7. Conclusions

7.1 Remarks

7.2 Original contribution of the author

7.3 Design and implementation contribution

7.4 General conclusions. Future work

PhD Thesis – Distributed Fingerprint Identification System

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Thesis Outline

This paper presents the main aspects involved in fingerprint recognition systems.

Biometric recognition refers to a number of methods for uniquely recognizing a person based on

one or more physical or behavioral characteristics. The most common biometric characteristics

or traits used by identification and verification (authentication) applications are: iris, hand

geometry, face, veins, voice, retina, handwriting, fingerprints etc. Among all the pos-sible

biometric traits, fingerprint is the most widely used. According to [1], fingerprint based

technology is the undisputed biometric leader, considering its market share of almost 67% in

2009. The same report forecasts that the annual biometric industry revenues will almost triple by

2014.

The main objectives of the thesis are presented in Chapter 1:

� the evaluation of the main algorithms involved in fingerprint recognition and image

processing;

� the design and the implementation of a partial fingerprint recognition application;

� the design and the implementation of a distributed fingerprint recognition system;

� the design and the implementation of a communication security module for the

proposed system;

� the design and the implementation of a mobile clients’ tracking using GPS.

Chapter 2 represents the state of the art of the main algorithms and methods involved in

fingerprint recognition process. A fingerprint image is a reproduction of the fingertip epi-dermis,

produced by pressing the finger against a solid surface. The most obvious structural feature of a

fingerprint is the pat-tern of alternative ridges and furrows (or valleys) [2]. Ridges’ width can

vary between 100 and 300 µm and the ridge/valley period is about 500 µm [3].

Fingerprint ridges begin to develop during the third to fourth month of fetal development.

They are fully developed by the seventh month and the probability of two fingerprints being

alike is 1 in 1.9 × 1015 [4]. Ridges and furrows are usually parallel, but they can also come to an

end or split, thus creating the two most widely used types of minutiae (small details):

terminations and bifurcations, respectively. There are other

types of minutiae such as: pores, short ridges (dots or islands),

cores, deltas etc. Although these other types of minutiae can

be considered, the FBI minutiae-coordinate model uses only

terminations and bifurcations [5]. Usually a minutia is defined

by the triplet {xi, yi, θi}, where xi and yi are the minutia

coordinates and θi is the angle between the tangent to the

ridge line at the minutia location and the horizontal axis (refer

to Fig. 1).

Besides minutiae, the other major

fingerprint feature class is represented by

the singularities, which are distinctive

patterns that the ridges form in a

fingerprint. Developed by Sir Edward

Henry in the late 1800s, the Henry

Classification System [6] is still used for

fingerprint classification in order to

a)termination b)bifurcation

Figura 1. Minutiae

arch loop whorl

Figure 2. Fingerprint typologies - Henry System

PhD Thesis – Distributed Fingerprint Identification System

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simplify the search and retrieval process in the recognition system’s data base. The main

fingerprint typologies presented in Fig. 2 are: arch, loop and whorl. These typologies are further

divided into subclasses (e.g. right loop and left loop, tented arch and plain arch).

The major steps involved in an automatic fingerprint recognition application are: the

fingerprint acquisition, the fingerprint image pre-processing, the feature extraction, the

fingerprint classification and the fingerprint matching.

There are many methods and technologies for each step involved in fingerprint recognition.

The fingerprint matching is the process of comparing an input fingerprint to preprocessed ones

(or templates) stored in a data base.

According to [7], fingerprint matching can be grouped into three major classes: (i)

correlation-based matching, (ii) minutiae-based matching and (iii) ridge feature-based matching.

Reference [8] states that the fingerprint recognition approaches can be grouped into five

categories: (i) based on singular points, (ii) structure-based, (iii) frequency-based, (iv) syntactic

or grammar-based, and (v) based on mathematical models.

High quality matching of complete fingerprints can be performed by many reasonable

algorithms. Matching of poor quality or partial samples is more difficult. Fingerprint images can

be affected by: high displacement and/or rotation, non-linear distortion (caused by representing a

3D shape in a 2D image), different pressure, skin condition and feature extraction errors [2].

Minutiae based recognition techniques are the most often used methods in fingerprint

recognition commercial applications because of their temporal performance, but they don’t

perform very well on low quality inputs [9] and might not be performed at all for partial

fingerprints. The loss of singular points (core and delta) is making singularity based recognition

and indexing techniques impossible [10].

In Chapter 3 a novel fuzzy logic algorithm based on correlating a minutiae set and the

regions between ridges is proposed for matching partial fingerprints.

The proposed fuzzy logic based algorithm for partial fingerprint matching combines

correlation-based and minutiae-based techniques and it is employed if no singularity is detected

in the fingerprint’s image. Direct application of correlation-based algorithms is computationally

very expensive, due to the large number of rotations and translations needed. In order to reduce

the computational time, the proposed algorithm tries to match two minutiae from the input with

two minutiae from the template. After aligning the two minutiae sets the two images can be

correlated.

In order to compare

the input to the template a

region coloring step is

required for the both image.

First, the fingerprint regions

have to be enclosed from the

background. This is done by

uniting the neighbor ridge

endings with straight lines.

Second, all the regions are

colored. This process is

similarly to the use of Microsoft Paint’s Fill with color Tool and it is performed by a labeling

algorithm [11]. Both the input and the template are colored as presented in Fig. 3.

a) b) c) d)

Figure 3. a)Template region enclosing, b)Input region enclosing,

c) Template region coloring, d)Input region coloring

PhD Thesis – Distributed Fingerprint Identification System

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The correlation degrees of

each input region are fuzzified by

using the well known membership

function presented in Fig. 4. The

regions are grouped according to

their membership degree and the

relative surface of each group is

determined (rshcd, rsmcd and

rslcd). A second fuzzification step

is performed for these 3 variables.

Based on a set of rules presented

in the thesis and on the inference

of rules, a global matching score

is determined (matchf).

Fig. 5 depicts the

defuzzification function of the

fuzzy output matchf. The L, M, H

intervals’ bounds were

experimentally set. Raising the

lower bounds of M and H

intervals means a higher security

level (or higher rejection rate).

The crisp values of the output

variable matchf are calculated by

the centre of gravity for singleton

method as in (1):

A distributed fingerprint

identification system is proposed

in Chapter 4. Here, the

requirements, the specifications

and the design of the system are

presented.

The proposed IDA System

is composed of a number of

mobile devices connected to

centralized processing units

(servers), which are responsible

for the enrollment and

recognition tasks. The mobile

devices are used for collecting

the personal data, for sending

Figure 4. Input membership function

Figure 5. Output membership function

������%� ∑ �� �������� ∗��

∑ ������

, (1)

where si is the strength of the ith

rule and t is the number of

the activated rules for the same input set.

Figure 6. The general architecture of IDA System

PhD Thesis – Distributed Fingerprint Identification System

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them to its assigned server along with the needed request identifier (enroll, verify or identify) and

for receiving the answers to their requests. The servers communicate with each other, being able

to forward unsolved recognition requests. The general architecture of the system is presented in

Fig. 6. For interconnecting the system’s servers, two different technologies were proposed: a

peer-to-peer topology and a MOM-JMS based topology (refer to Fig. 7 and 8, respectively).

The mobile client uses an experimental device to read the fingerprints. A mobile phone

running a Java ME application is used to connect to the device (via Bluetooth) and to retrieve the

fingerprint. See Fig. 9 a), b), c), d). The fixed client application is implemented in Java 2 SE and

it uses an optical fingerprint sensor (Wison Corp OR100) presented in Fig. 9 e).

Figure 9. Mobile and fixed client applications

The distributed applications that compose proposed system was designed with the help of

the following UML diagrams: use-case, sequence, task, activity and class. Before implementing

the proposed applications, a verification of the models is required, in order to avoid deadlocks,

since each distributed application deals with a fairly large number of synchronized threads. The

best suited method for this purpose is the use of Petri Nets.

Figure 7. Peer-to-peer servers’ topology

Figure 8. MOM based servers’ topology

a) b) c)

d) e)

PhD Thesis – Distributed Fingerprint Identification System

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In Chapter 5, the implementation details of the distributed fingerprint identification

system are given. This chapter focuses on presenting the main technologies and protocols used to

implement the system: GSM/GPRS (TCP/IP), Bluetooth (RFCOMM), JNI, MySQL and

VeriFinger SDK. The main aspects of the software implementation in Java SE and ME are also

given.

The proposed distributed fingerprint recognition system was tested from the next points

of view:

- functionality tests,

- error handling tests,

- temporal performances tests.

Chapter 6 proposes two extension modules for enhancing the system’s capabilities. The

first extension module is responsible for ensuring the system’s communication security. The

encryption methods considered (and their temporal performance for encrypting – sending -

decrypting 81KB of data) are presented in Table 1.

Table 1. Temporal performances

Encryption technique Block size Temporal performances

fără criptare 1024 B, 81 KB < 7 ms

RSA (JCE) 64 B 3,5 sec

DES (JCE) 1024 B 285 ms

AES (JCE) 1024 B 310 ms

SSL (JSSE) 1024 B 80 ms

The second extension module needs involves the use of GPS modules, integrated in the

mobile client applications, in order to track the terminals’ position by using a web application

developed on top of Google Maps.

A novel Diffie-Hellman based method is also proposed in this chapter. The method is

used to secure the communication between the terminals of a message exchanger system. The

keys used to encrypt the communication are generated from the information extracted for the

GPS position and from the fingerprint image.

The conclusions of the thesis are presented in Chapter 7. The main original contribution

of this paper can be synthesized as follows:

- A new partial fingerprint recognition method was proposed [12].

- A novel encryption system and method were proposed [13].

- The original architecture and implementation for a distributed fingerprint

identification system were presented.

The proposed IDA System is scalable (especially in the MOM-JMS-based architecture)

and has a modular implementation. Due to its characteristics, IDA System can be easily

customized to meet the requirements of practical applications. Based on the studies presented in

this paper, three commercial products were developed:

- fingerprint-based access code generator;

- radio remote control secured with fingerprint recognition;

- GPS tracking system secured with fingerprint recognition.

PhD Thesis – Distributed Fingerprint Identification System

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Selective Bibliography

The thesis contains 140 references from which the next were used in this abstract:

[1] “Biometrics Market Report 2009-2014”. International Biometrics Group, 2009.

[2] D. Maltoni, D. Maio, A. K. Jain, S. Prabhakar, “Handbook of Fingerprint Recognition”,

Springer-Verlag, New York, 2003.

[3] J.D.Stosz, L.A. Alyea, ”Automated System forFingerprint Authentication Using Pores and

Ridge Structure”, Proc. of SPIE (Automatic Systems for the Identification and Inspection of

Human), vol. 2277, 1994, pp. 210-223.

[4] W. F. Leung, S. H. Leung, W.H. Lau, A. Luk, “Fingerprint Recognition Using Neural

Network, Neural Networks For Signal Processing”, Proceedings Of The 1991 IEEE

Workshop.

[5] Wegstein ”An Automated Fingerprint Identification System”, U.S. Government Publication,

Washington, DC: U.S. Dept. of Commerce, National Bureau of Standards, 1982.

[6] E. R. Henry, “Classification and Uses of Fingerprints”, London: Routledge, 1900.

[7] D. Maltoni, “A Tutorial on Fingerprint Recognition”, Biometric Systems Laboratory - DEIS -

University of Bologna, 2005.

[8] S. C. Dass, A. K. Jain, “Fingerprint Classification Using Orientation Field Flow Curves”,

ICVGIP 2004, p. 650-655.

[9] A. N. Marana, A. K. Jain, “Ridge-Based Fingerprint Matching Using Hough Transform”,

Computer Graphics and Image Processing, SIBGRAPI, 2005, p. 112-119.

[10] H. Le, D. Bui, ”Online fingerprint identification with a fast and distortion tolerant hashing”,

Journal of Information Assurance and Security 4, 2009, p. 117-123.

[11] Yang, X. D., “An Improved Algorithm for Labeling Connected Components in a Binary

Image”, TR 89-981, 1989.

[12] Miron, R., Letia, T. OSIM Patent Request for – Partial Fingerprint Recognition Method –

A/10014/2010 from 29.08.2010.

[13] Astilean, A., Folea, S., Avram, C., Hulea, M., Miron, R., Letia, T., OSIM Patent Request

for – Method and System for Ensuring the Communication Security between Fixed and

Mobile Terminals – A/10037/2010 from 08.12.2010.