Search or add a thesis

Advanced Search (Beta)
Home > An Improved Latent Fingerprints Feature Extraction and Matching Algorithm

An Improved Latent Fingerprints Feature Extraction and Matching Algorithm

Thesis Info

Access Option

External Link

Author

Muhammad Irfan Arshad

Program

PhD

Institute

University of Engineering and Technology

City

Taxila

Province

Punjab

Country

Pakistan

Thesis Completing Year

2015

Thesis Completion Status

Completed

Subject

Electrical Engineering

Language

English

Link

http://prr.hec.gov.pk/jspui/bitstream/123456789/9951/1/8.%20PhD_Thesis_Irfan_%2809-UET-PhD-EE-37%29.pdf

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676727699452

Similar


Latentfingerprintsaretheimpressionsofpartialridgesleftonthesurfaceofobjects touched unintentionally at crime scenes and constitute a valuable source of evidence in law enforcement agencies to helpsolve crimes. However,majority of the processing (marking regionof interest(ROI),singularpoints(SP),orientationfieldandminutiaepoints)for latentprintsidentificationisdonemanuallybyforensicexperts.Theexistingmethods involve forensicexpertstomanually markthe featuresin latentandtheninput ittothe systemforautomaticmatchingwithreferenceprints.Thematcherreturnsalistof candidates thatare manuallycheckedbyexperts totake final decision.Thepracticeof manuallymarkingfeaturesinlatentsislaborious,timeconsumingandhumandependent whichmayresultsinwrongidentification.Thereforethereisaneedtoautomatethis process to avoid aforesaidconstraints. This thesis describes an automatedapproach of segmentation andenhancement for latent fingerprints identification.Currently,afew attemptshavebeenmade inthisrespectand still remain a challenging problem due to: (i) poor quality, (ii) small friction ridge area,(iii) presence of non-linear distortion,(iv) blurringor smudging,and(v) complexbackground noise. In this research, an algorithm for automated segmentation of latent fingerprints is proposed. The latent image datais classifiedintoclusters using K-means clustering techniquewhich results in pixels having similar characteristicsto fit in one cluster (foreground) while pixels having opposite characteristicsto other cluster (background).Tophat filtering is appliedto enhance the clustered data and mask is generated on the basis of this enhanced information. Segmentation is achievedby applying thegenerated maskon latent image.The proposed algorithm forsegmentationof latent fingerprintsisautomatedwithout anysortof human involvement.Performanceofproposedalgorithmisevaluatedbycomputingthemissed detection rate (MDR) and false detection rate (FDR) and comparison of proposed method withotherexistingalgorithmsisdone.SimulationresultsonNISTSD-27(databaseof latentfingerprintimagescontaining258latentfingerprintsalongwiththeirmatedrolled prints)showsignificantperformanceenhancementofproposedmethodhavingaverage MDR and FDR of 4.77% and 26.06% respectively. Furthermore, subjective comparison is made usingvisual segmentation reliability(VSR)which istheratioof intersectional area ofautomated and ground truthlatent to manually marked segmented latent. VSR approaches to 90% for good quality images, 70-80% for bad quality images and 50-60% for ugly qualityimages.Matchingperformance isimprovedwhen thesegmentedinputis applied to commercial-off-the-shelf(COTS tenprint)matcherascompared with un- segmentedinput. Another contribution of proposed research is towards the enhancement of latent fingerprints. Enhancement of segmented latent isperformed using Gabor filter bank. It has five image-dependent-parameterslikeorientation ∅ ,standarddeviations ?�and ?�ofthe Gaussian function,timeperiodTandthe convolutionmasksize.Theselectionof these parametersplaysacrucial roleinfingerprintenhancementspecificallytheorientation∅ andstandarddeviation ?� and ?� .Thelatentimageis dividedintoblocks of WxW centered at pixel (i,j)and gradients ��� and ��� along x-axis andy-axis are computedby applying Sobel operator at everypixel.Orientation ∅ is computedon the basis ofthese computed gradients. Ridge frequency F(i, j) is estimated by calculating the grey level value of each pixel, housed in the block, and is projected in a direction perpendicular to the local ridgeorientationandridgespacingS(i,j).Animprovementinfrequencyestimationis achievedbyintroducingGaussianlowpassfilterthatminimizesthenoiselevels.Ridge orientation ∅ and frequency F(i, j) is used to design an even-symmetric Gabor filter. Spatial convolution of thelatentfingerprint withGaborfilter isperformedtogenerateenhanced latentimage.SimulationresultsonNISTSD-27showthatimprovementinmatchingis increased 11% in comparison toautomatedlatent fingerprint segmentation and enhancementalgorithmby Zhang et al in 2013.
Loading...

Similar Thesis

Showing 1 to 20 of 100 entries
TitleAuthorSupervisorDegreeInstitute
PhD
University of Engineering and Technology, Taxila, Pakistan
BS
University of Management and Technology, Lahore, Pakistan
MSc
Riphah International University, Faisalabad, Pakistan
RCS
COMSATS University Islamabad, Islamabad, Pakistan
MS
International Islamic University, Islamabad, Pakistan
PhD
International Islamic University, Islamabad, Pakistan
BS
International Islamic University, Islamabad, Pakistan
PhD
University of Engineering and Technology, Taxila, Pakistan
BS
International Islamic University, Islamabad, Pakistan
BS
International Islamic University, Islamabad, Pakistan
MSc
International Islamic University, Islamabad, Pakistan
University of Management and Technology, Lahore, Pakistan
MSc
Riphah International University, Faisalabad, Pakistan
PhD
National University of Sciences & Technology, Islamabad, Pakistan
MS
Riphah International University, Lahore, Pakistan
MSc
Riphah International University, Faisalabad, Pakistan
University of Engineering and Technology, Lahore, Pakistan
PhD
International Islamic University, Islamabad, Pakistan
PhD
National University of Computer and Emerging Sciences, Islamabad, Pakistan
Mphil
Quaid-i-Azam University, Islamabad, Pakistan
TitleAuthorSupervisorDegreeInstitute
Showing 1 to 20 of 100 entries

Similar News

Loading...

Similar Articles

Loading...

Similar Article Headings

Loading...