UNIVERSITI MALAYSIA PAHANG

Transcription

1 IMAGE ENHANCEMENT AND SEGMENTATION ON SIMULTANEOUS LATENT FINGERPRINT DETECTION ROZITA BINTI MOHD YUSOF MASTER OF COMPUTER SCIENCE UNIVERSITI MALAYSIA PAHANG

2 IMAGE ENHANCEMENT AND SEGMENTATION ON SIMULTANEOUS LATENT FINGERPRINT DETECTION ROZITA BINTI MOHD YUSOF THESIS SUBMITTED IN FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF THE DEGREE OF MASTER OF COMPUTER SCIENCE FACULTY OF COMPUTER SYSTEMS & SOFTWARE ENGINEERING UNIVERSITI MALAYSIA PAHANG APRIL 2015

3 SUPERVISOR S DECLARATION I hereby declare that I have checked this thesis and in my opinion this thesis is satisfactory in terms of scope and quality of the award of the degree of Master of Science (Computer). Signature : Name of Supervisor : ASSOC. PROF. DR. NORROZILA BINTI SULAIMAN Position : SENIOR LECTURER FACULTY OF COMPUTER SYSTEMS & SOFTWARE ENGINEERING, UNIVERSITI MALAYSIA PAHANG Date : APRIL, 2015

4 ii STUDENT S DECLARATION I hereby declare that the work in this thesis is my own except for quotations and summaries which have been duly acknowledged. The thesis has not been accepted for any degree and is not concurrently submitted for award of another degree. Signature : Name : ROZITA BINTI MOHD YUSOF ID Number : MCC10002 Date : 09 APRIL, 2015

5 vii TABLE OF CONTENTS SUPERVISOR S DECLARATION STUDENT S DECLARATION DEDICATION ACKNOWLEDGEMENTS ABSTRACT ABSTRAK TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS Page i ii iii iv v vi vii x xi xiii CHAPTER 1 INTRODUCTION 1.1 Background of The Research Problem Statement Research Objectives Scope of The Study Organization of Thesis 7 CHAPTER 2 LITERATURE REVIEW 2.1 Introduction Background and Previous Work Latent Fingerprint Image Enhancement Image Normalization through Filtering Technique Intensity Adjustment Fingerprint Segmentation Clustering Methods Fingerprint Orientation Features 13

6 viii Distal Phalanges Dataset Summary 20 CHAPTER 3 FINGERPRINT SEGMENTATION 3.1 Introduction Operation Steps of Pre-Processing (Segmentation) Step 1: Image Normalization Step 2: Intensity Adjustment Step 3: Binarization Step 4: Clustering Step 5: Region of Interest Candidate Selection Fingerprint Orientation Distal Phalanges Segmentation Accuracy Estimation Comparison with Ground Truth Matching Performance Evaluation The Sensitivity and Specificity Measurement False Reject Rate Estimation Summary 38 CHAPTER 4 IMPLEMENTATION AND RESULTS 4.1 Introduction The Experimental Result Normalization Image Result Comparison of Contrast Adjustment Segmentation Accuracy Result Comparison with Ground Truth 46

7 ix Matching Performance Result Summary 53 CHAPTER 5 CONCLUSION AND RECOMMENDATIONS 5.1 Introduction Research Objectives Achievement Recommendation Potential Improvement 55 REFERENCES 57 APPENDICES 68 BIODATA OF THE AUTHOR 121 LIST OF PUBLICATIONS 122

8 x LIST OF TABLES Table No. Title Page 1.1 High profile erroneous cases of latent prints identification The brightness level of AltHE Comparison of Segmentation Error Result The Accuracy ofscore Matched (%) using SLFS Algorithm, Manual Segmentation and without Segmentation. 50

9 xi LIST OF FIGURES Figure No. Title Page 1.1 Types of fingerprint (a) rolled fingerprint, (b) plain fingerprint 2 and (c) Slap fingerprints 2.1 An example result generated by the segmentation algorithm on 12 slap fingerprint (adopted from Hodl et al. (2009)). 2.2 The part of fingerprint The example of (a) SLF image with clear of ridge flow of distal 17 phalanges portion (b) SLF image with bluring texture. 2.4 The example of (a) unclear and noises of SLF image (b) SLF 18 image with distortion of ridge texture. 2.5 The example of (a) dry SLF image (b) wet SLF image The Segmentation Framework The brightness level of AltHE The equalized image based on AltHE technique The illustration of closing operation process The illustration of result of sub-region after clustering process The dominant directions of ridge orientation (a) Before applied eigenvector (b) masks of the candidate regions 34 are rotated using eigenvector. 4.1 SLFS Algorithm operation for ROI detection of SLF images A distal phalanx portion of SLF image from IIITD-SLF database 42 (a) Image before normalizes process, (b) image after normalize process using Gabor and Fast Fourier Transform. 4.3a (a) Original image of SLF image (b) HE result b (c) Auto contrast result (d) the AltHE result The ridge orientation map using directional method on three contrast adjustment method: (a) on HE image, (b) on auto 45

10 xii contrast image and (c) on proposed method using AltHE image result. 4.5 Comparison of segmentation result based on frequency of missed identified regions (MI). 4.6 Comparison of segmentation result based on frequency of false identified regions (FI). 4.7 Comparison of segmentation result based on frequency of correctly identified regions (CI). 4.8 Comparison of sensitivity results between SLFS algorithm, manual segmentation and without segmentation via VeriFinger SDK model. 4.9 Comparison of Specificity results between SLFS algorithm, manual segmentation, and matching without segmentation Comparison between SLFR model with the HE and SFS methods, SLFR model without SFS method and VeriFinger model (non-segmentation) based on Score Matched results

11 xiii LIST OF ABBREVIATIONS AVE-C AFIS AltHE CI CN Analysis, Comparison, Evaluation and Verification Automated Fingerprint Identification System Alteration Histogram Equalization Correctly identified regions Crossing Number C# C-Sharp FAR FI FIR FFT FRR HE MI MIR NIST ROI SLF SLFS False Accept Rate False identified regions False Identified Rate Fast Fourier Transform False Reject Rate Histogram Equalization Missed identified regions Missed Identification Rate National Institute of Standards and Technology Region of interest Simultaneous Latent Fingerprint Simultaneous Latent Fingerprint Segmentation