int. j. engg. res. & sci. & tech. 2014 p rajasekar et al ... · using verilog hdl. ......

This article can be downloaded from http://www.ijerst.com/currentissue.php

271

Int. J. Engg. Res. & Sci. & Tech. 2014 P Rajasekar et al., 2014

ISSN 2319-5991 www.ijerst.com

Vol. 3, No. 2, May, 2014

© 2014 IJERST. All Rights Reserved

Research Paper

RECONFIGURABLE ACCELERATOR FOR BIOMETRICSEARCH ENGINE USING FPGA AND MATLAB

P Rajasekar1*, G Sundar2, P Sebastin Ashok1 and S Shyamaladevi1

*Corresponding Author: P Rajasekar, � [email protected]

Biometric technologies are becoming the foundation of an extensive array of highly secureidentification and personal verification solutions. One of the most significant parts of aninvestigation is fingerprinting. Today, the internet and high memory computers allow lawenforcement officials to place prints in a massive database run by the Federal Bureau ofInvestigation (FBI) that can be accessed from anywhere in the world. While we interface fingerprintsensor with computers, several constraints appear, such as memory limitations, time ofcomputation, recognition robustness and power consumption. In order to get benefit of theperformance of recent FPGA devices, such as amount of memory, number of logic blocks, lowpower consumption and faster clocks, this paper proposes new hardware implementationalgorithms for fingerprinting technology.

Keywords: Biometric technologies, FPGA, FBI, Memory

INTRODUCTION

Biometrics are automated methods of recognizinga person based on a physiological or behavioralcharacteristic. Among the features measured are;face fingerprints, hand geometry, handwriting, iris,retinal, vein, and voice. Biometric technologiesare becoming the foundation of an extensivearray of highly secure identification and personalverification solutions. As the level of securitybreaches and transaction fraud increases, theneed for highly secure identification and personalverification technologies is becoming apparent.

Biometric-based solutions are able to providefor confidential financial transactions andpersonal data privacy. The need for biometricscan be found in federal, state and localgovernments, in the military, and in commercial

1 M.E VLSI Design, ECE Department, Sembodai Rukmani Varatharajan Engineering College.2 Assistant Professor, ECE Dept, Sembodai Rukmani Varatharajan Engineering College Vedaranyam, Tamilnadu, India.

applications. Enterprise-wide network securityinfrastructures, government IDs, secureelectronic banking, investing and other financialtransactions, retail sales, law enforcement, andhealth and social services are already benefitingfrom these technologies. Fingerprint recognitionis one of the most common techniques used forbiometric identification. Also one of the mostsignificant parts of an investigation isfingerprinting. Seen as a technology that is alogical replacement for antiquated andcumbersome personal identification numbers(PINs) and passwords, biometrics is a moresecure alternative to enhance individualidentification accuracy and system security.

Nowadays the research effort in fingerprintalgorithms is focused on improving their


272


performances, basically increasing the reliabilityand reducing the error rates. Usually theimplementation is based on a high-performancemicroprocessor, such as a desk computer, ableto work at frequencies in the GHz range. Thealgorithm runs on a microprocessor thatsequentially executes the routines involved in thefingerprint processing. Recent advances in thefield microelectronics have improved themicroprocessor computational power whichallows algorithms to run with high accuracy with-out increasing the execution times. However, westill depend on computers for all external worldtechnical interfacing devices.

FINGERPRINTS AS A BIOMETRIC

Among all biometric traits, fingerprints have oneof the highest levels of reliability and have beenextensively used by forensic experts in criminalinvestigations. A fingerprint refers to the flow ofridge patterns in the tip of the finger. The ridgeflow exhibits anomalies in local regions of thefingertip (Figure 1), and it is the position andorientation of these anomalies that are used torepresent and match fingerprints.

Although not scientifically established,fingerprints are believed to be unique acrossindividuals, and across fingers of the sameindividual. Even identical twins having similarDNA, are believed to have different fingerprints.Traditionally, fingerprint patterns have beenextracted by creating an inked impression of thefingertip on paper. The electronic era has usheredin a range of compact sensors that provide digitalimages of these patterns. These sensors can beeasily incorporated into existing computerperipherals like the mouse or the keyboard(Figure 2), thereby making this mode ofidentification a very attractive proposition. Thishas led to the increased use of automaticfingerprint-based authentication systems in bothcivilian and law enforcement applications.

Figure 1: Fingerprint Image Thinning

Figure 2: Minutia Points(Bifurcation-Red, Termination-Blue)

A. Fingerprint RepresentationThe uniqueness of a fingerprint is determined bythe topographic relief of its ridge structure andthe presence of certain ridge anomalies termedas minutiae points. Typically, the globalconfiguration defined by the ridge structure isused to determine the class of the fingerprint,while the distribution of minutiae points is used


273


to match and establish the similarity between twofingerprints. Automatic fingerprint identificationsystems, that match a query print against a largedatabase of prints (which can consist of millionsof prints), rely on the pattern of ridges in the queryimage to narrow their search in the database(fingerprint indexing), and on the minutiae pointsto determine an exact match (fingerprintmatching).

B. Extraction of Minutiae and Eliminationof False MinutiaeThe accuracy of the extraction of minutiae frombinary fingerprint image depends on the successof enhancement and thinning processes. If ridgeand valley structures of the fingerprint image aredamaged during these processes, it causes toextract false minutiae points. As a result, it is veryimportant to gain best results from theseprocesses in order to extract minutia correctly.

HAMMING DISTANCE

In information theory, the Hamming distancebetween two strings of equal length is the numberof positions at which the corresponding symbolsare different. In another way, it measures theminimum number of substitutions required tochange one string into the other, or the minimumnumber of errors that could have transformed onestring into the other.

Algorithm ExampleThe Python function hamming distance()computes the Hamming distance between twostrings (or other iterable objects) of equal length,by creating a sequence of Boolean valuesindicating mismatches and matches betweencorresponding positions in the two inputs, andthen summing the sequence with False and Truevalues being interpreted as zero and one.

def hamming_distance(s1, s2)#Return the Hamming distance between equal-length sequences

if len(s1) != len(s2)raise ValueError (“Undefined for sequences ofunequal length”)

return sum(ch1 != ch2 for ch1, ch2 in zip(s1,s2))

The following C function will compute theHamming distance of two integers (consideredas binary values, that is, as sequences of bits).The running time of this procedure is proportionalto the Hamming distance rather than to thenumber of bits in the inputs. It computes thebitwise exclusive or of the two inputs, and thenfinds the Hamming weight of the result (thenumber of nonzero bits) using an algorithm ofWegner that repeatedly finds and clears thelowest-order nonzero bit.

unsigned hamdist(unsigned x, unsigned y)

{unsigned dist = 0, val = x ^ y; // XOR

// Count the number of set bits while(val)

{++dist; val &= val - 1;}

return dist;}

PROPOSED ARCHITECTURE

Figure 3 shows the architecture of the proposedsystem. Grey scale eye images are capturedand processed to generate fingerprint templatedatabase which will be stored on Rom. For lagerdatabases, off-chip ROM will be interfaced toFPGA. Small databases can be stored on chip.Live template is compared with the templates inthe database by calculating hamming distancebetween them. Since orientation of iris changestime to time best match is found by shifting thedata template 2 bits left and then performscomparison operation again with the livetemplate. If match found search will be stoppedafter generating the result else search willcontinue for the entire database. Result can bedisplayed on monitor or through LED an LCD.Matching module has the internal structure asfollows:

� Memory Module

� HD modules

� Adder tree

� Comparator and display


274


Figure 3: System Architecture

Figure 4: Adder Tree

Implementation ConceptsSample database is created by generating thetemplates for 10 sample eye images taken fromMMU1 database. Segmentation, Normalizationand Encoding is done using matlab open sourcecode. Matching module is implemented usingverilog HDL. Proposed design is implementedusing adders, shifter, and magnitude comparator.Xilinx isim (Ise 12.4) tool is used to generatesimulation results. When live template is givenas input, and when reset is not asserted, searchstarts. Search starts at first data template in thedatabase. Live template is shifted 2 bits left andagain compared with the stored template. Total10 left shifts and 10 right shifts are performed fordisplacement alignment. In this way HD iscalculated between live template and eachdatabase template for 21 times. If HD<=Threas-hold, m (match) will be asserted high.

CONCLUSION & FUTURE SCOPE

Low cost and fast execution are importantparameters for real time authentication systems.The main purpose of the work described in thispaper is to implement most time consuming partof fingerprint recognition algorithm on a low-costFPGA. The proposed design is implementedusing verilog HDL. This design is suitable forsmall low cost applications. Future improvementsin the system can be done by implementing thefeature extraction task on hardware which canfurther reduce the authentication time. Insteadof using Matlab we can use Micro blaze soft coreprocessor and can implement the iris localizationand iris normalization in C. By this we canimplement entire system on single chip.

REFERENCES

1. Daugman J G (2003), “The Importance ofBeing Random: Statistical Principles of IrisRecognition”, Pattern Recognition, Vol. 36,pp. 279-291.

2. Daugman J G (2004), “How Iris RecognitionWorks IEEE Trans”, Circuits Syst. VideoTechnology, Vol. 14, No. 1, pp. 21-30.

3. James Wayman, Anil K Jain, Davide Maltoniand Dario Maio (2005), “Biometric Systems:Technology, Design and PerformanceEvaluation”, Springer-Verlag Limited,London.

4. Maitane Barrenechea, Jon Altuna and MiguelSan Miguel (2007), “A Low Cost FPGA-Based Embedded Fingerprint VerificationEmbedded Fingerprint Verification andMatching System”, Fifth Workshop onIntelligent Solutions in Embedded Systems(WISES 07).

5. P Wildes, S C Hsu, R J Kolczynski, R Matey,J C Asmuth and S E McBride (1996),“Automated, Noninvasive Iris RecognitionSystem and Method”, U.S. Patent, No. 5, pp.572-596.

6. Raimond Thai (2003), “Fingerprint ImageEnhancement and Minutiae Extraction”,Master’s thesis, University of WesternAustralia.


275


7. Ryan N Rakvic, Brandley J Ulis, Randy PBroussard, Robert W Ives and Neil Steiner(2009), “Parellelizing Iris Recognition, IEEETransactions on Information Forensics andSecurity”, Vol. 4, No. 4, December 2009.

8. Shenglin Yang, Kazuo Sakiyama, and IngridVerbauwhedem (2006), “Efficient and SecureFingerprint Verification for Embedded

Devices”, URASIP Journal on Applied SignalProcessing, pp. 1-11.

9. ZHOU Hu-Lin, XIE Mei (2010), “Iris BiometricProcessor Enhanced Module FPGA basedDesign”, 2010 Second InternationalConference on Computer Modeling andSimulation.

int. j. engg. res. & sci. & tech. 2014 p rajasekar et al ... · using verilog hdl. ......

Documents