Identifying individuals from average quality fingerprint reference templates, when the best do not provide the best results !

International audienceThe fingerprint is one of the most used biometric modalities because of its persistence, uniqueness characteristics and ease of acquisition. Nowadays, there are large country-sized fingerprint databases for identification purposes, for border access controls and also for Visa issuance procedures around the world. The objective usually is to identify an input fingerprint among a large fingerprint database. In order to achieve this goal, different fingerprint pre-selection, classification or indexing techniques have been developed to speed up the research process to avoid comparison of the input fingerprint template against each fingerprint in the database. Although these methods are fairly accurate for identification process, we think that all of them assume the hypothesis to have a good quality of the fingerprint template for the first step of enrollment. In this paper, we show how the quality of reference templates can impact the performance of identification algorithms. We collect information and implement differents methods from the state of the art of fingerprint identification. Then, for these differents methods, we vary the quality of reference templates by using NFIQ2 metric quality. This allowed us to build a benchmark in order to evaluate the impact of these different enrollment scenarios on identification

An Efficient Fingerprint Identification using Neural Network and BAT Algorithm

The uniqueness, firmness, public recognition, and its minimum risk of intrusion made fingerprint is an expansively used personal authentication metrics. Fingerprint technology is a biometric technique used to distinguish persons based on their physical traits. Fingerprint based authentication schemes are becoming increasingly common and usage of these in fingerprint security schemes, made an objective to the attackers. The repute of the fingerprint image controls the sturdiness of a fingerprint authentication system. We intend for an effective method for fingerprint classification with the help of soft computing methods. The proposed classification scheme is classified into three phases. The first phase is preprocessing in which the fingerprint images are enhanced by employing median filters. After noise removal histogram equalization is achieved for augmenting the images. The second stage is the feature Extraction phase in which numerous image features such as Area, SURF, holo entropy, and SIFT features are extracted. The final phase is classification using hybrid Neural for classification of fingerprint as fake or original. The neural network is unified with BAT algorithm for optimizing the weight factor

A Survey of Fingerprint Classification Part I: Taxonomies on Feature Extraction Methods and Learning Models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.Research Projects CAB(CDTI) TIN2011-28488 TIN2013-40765Spanish Government FPU12/0490

A survey of fingerprint classification Part I: taxonomies on feature extraction methods and learning models

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented.This work was supported by the Research Projects CAB(CDTI), TIN2011-28488, and TIN2013-40765-P.

Fast fingerprint verification using sub-regions of fingerprint images.

Chan Ka Cheong.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 77-85).Abstracts in English and Chinese.Chapter 1. --- Introduction --- p.1Chapter 1.1 --- Introduction to Fingerprint Verification --- p.1Chapter 1.1.1 --- Biometrics --- p.1Chapter 1.1.2 --- Fingerprint History --- p.2Chapter 1.1.3 --- Fingerprint characteristics --- p.4Chapter 1.1.4 --- A Generic Fingerprint Matching System Architecture --- p.6Chapter 1.1.5 --- Fingerprint Verification and Identification --- p.8Chapter 1.1.7 --- Biometric metrics --- p.10Chapter 1.2 --- Embedded system --- p.12Chapter 1.2.1 --- Introduction to embedded systems --- p.12Chapter 1.2.2 --- Embedded systems characteristics --- p.12Chapter 1.2.3 --- Performance evaluation of a StrongARM processor --- p.13Chapter 1.3 --- Objective -An embedded fingerprint verification system --- p.16Chapter 1.4 --- Organization of the Thesis --- p.17Chapter 2 --- Literature Reviews --- p.18Chapter 2.1 --- Fingerprint matching overviews --- p.18Chapter 2.1.1 --- Minutiae-based fingerprint matching --- p.20Chapter 2.2 --- Fingerprint image enhancement --- p.21Chapter 2.3 --- Orientation field Computation --- p.22Chapter 2.4 --- Fingerprint Segmentation --- p.24Chapter 2.5 --- Singularity Detection --- p.25Chapter 2.6 --- Fingerprint Classification --- p.27Chapter 2.7 --- Minutia extraction --- p.30Chapter 2.7.1 --- Binarization and thinning --- p.30Chapter 2.7.2 --- Direct gray scale approach --- p.32Chapter 2.7.3 --- Comparison of the minutiae extraction approaches --- p.35Chapter 2.8 --- Minutiae matching --- p.37Chapter 2.8.1 --- Point matching --- p.37Chapter 2.8.2 --- Structural matching technique --- p.38Chapter 2.9 --- Summary --- p.40Chapter 3. --- Implementation --- p.41Chapter 3.1 --- Fast Fingerprint Matching System Overview --- p.41Chapter 3.1.1 --- Typical Fingerprint Matching System --- p.41Chapter 3.1.2. --- Fast Fingerprint Matching System Overview --- p.41Chapter 3.2 --- Orientation computation --- p.43Chapter 3.21 --- Orientation computation --- p.43Chapter 3.22 --- Smooth orientation field --- p.43Chapter 3.3 --- Fingerprint image segmentation --- p.45Chapter 3.4 --- Reference Point Extraction --- p.46Chapter 3.5 --- A Classification Scheme --- p.51Chapter 3.6 --- Finding A Small Fingerprint Matching Area --- p.54Chapter 3.7 --- Fingerprint Matching --- p.57Chapter 3.8 --- Minutiae extraction --- p.59Chapter 3.8.1 --- Ridge tracing --- p.59Chapter 3.8.2 --- cross sectioning --- p.60Chapter 3.8.3 --- local maximum determination --- p.61Chapter 3.8.4 --- Ridge tracing marking --- p.62Chapter 3.8.5 --- Ridge tracing stop criteria --- p.63Chapter 3.9 --- Optimization technique --- p.65Chapter 3.10 --- Summary --- p.66Chapter 4. --- Experimental results --- p.67Chapter 4.1 --- Experimental setup --- p.67Chapter 4.2 --- Fingerprint database --- p.67Chapter 4.3 --- Reference point accuracy --- p.67Chapter 4.4 --- Variable number of matching minutiae results --- p.68Chapter 4.5 --- Contribution of the verification prototype --- p.72Chapter 5. --- Conclusion and Future Research --- p.74Chapter 5.1 --- Conclusion --- p.74Chapter 5.2 --- Future Research --- p.74Bibliography --- p.7

Efficient software development for microprocessor based embedded system.

Tang Tze Yeung Eric.Thesis submitted in: July 2003.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 69-75).Abstracts in English and Chinese.ABSTRACT --- p.IIACKNOWLEDGMENT --- p.IIChapter 1 --- INTRODUCTION --- p.1Chapter 1.1 --- Embedded System --- p.1Chapter 1.2 --- Embedded Processor --- p.1Chapter 1.3 --- Embedded System Design --- p.3Chapter 1.3.1 --- Current Embedded System Design Challenges --- p.3Chapter 1.3.2 --- Embedded System Design Trend --- p.4Chapter 1.4 --- Efficient Software Development for Microprocessor --- p.8Chapter 1.4.1 --- Efficient Software Development Methodology --- p.8Chapter 1.5 --- Thesis Organization --- p.10Chapter 2 --- SOURCE CODE OPTIMIZATION --- p.11Chapter 2.1 --- Source Code Optimization Strategy --- p.11Chapter 2.2 --- Source Code Transformations --- p.12Chapter 2.2.1 --- Strength Reduction --- p.12Chapter 2.2.2 --- Function Inlining --- p.13Chapter 2.2.3 --- Table Lookup --- p.13Chapter 2.2.4 --- Loop Transformations --- p.13Chapter 2.2.5 --- Software Pipelining --- p.15Chapter 2.2.6 --- Register Allocation --- p.17Chapter 2.3 --- Case Study: Source Code Optimization on the StrongARM (SA1110) Platform --- p.18Chapter 2.3.1 --- StrongARM architecture --- p.18Chapter 2.3.2 --- StrongARM pipeline hazard illustration --- p.20Chapter 2.3.3 --- Source Code Optimization on StrongARM --- p.21Chapter 2.3.4 --- Instruction Set Optimization of StrongARM --- p.27Chapter 2.4 --- Conclusion --- p.32Chapter 3 --- FLOAT-TO-FIXED OPTIMIZATION --- p.33Chapter 3.1 --- Introduction to Fixed-point --- p.34Chapter 3.1.1 --- Fixed-point representation --- p.34Chapter 3.1.2 --- Fixed-point implementation --- p.35Chapter 3.1.3 --- Mathematical functions implementation --- p.38Chapter 3.2 --- Case Study: Fingerprint Minutiae Extraction Algorithms on the Strong ARM platform --- p.41Chapter 3.2.1 --- Fingerprint Verification Overview --- p.42Chapter 3.2.2 --- Fixed-point Implementation of Fingerprint Minutiae Extraction Algorithm --- p.49Chapter 3.2.3 --- Experimental Results --- p.51Chapter 3.3 --- Conclusion --- p.56Chapter 4 --- DOMAIN SPECIFIC OPTIMIZATION --- p.57Chapter 4.1 --- Case Study: Font Rasterization on the Strong ARM platform --- p.57Chapter 4.1.1 --- Outline Font --- p.57Chapter 4.1.2 --- Font Rasterization --- p.59Chapter 4.1.3 --- Experiments --- p.63Chapter 4.2 --- Conclusion --- p.66Chapter 5 --- CONCLUSION --- p.67BIBLIOGRAPHY --- p.6