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
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 Table of Contents 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 i
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 ii
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 {x i, y i, θ i }, where xi and y i are the minutia coordinates and θ i 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 1
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 a) b) c) d) Figure 3. a)template region enclosing, b)input region enclosing, c) Template region coloring, d)input region coloring 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. 2
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 s i is the strength of the i th rule and t is the number of the activated rules for the same input set. Figure 6. The general architecture of IDA System 3
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). Figure 7. Peer-to-peer servers topology Figure 8. MOM based servers topology 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). a) b) c) d) 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. 4
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. 5
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. 6