Fusion of fingerprint presentation attacks detection and matching: a real approach from the LivDet perspective

The liveness detection ability is explicitly required for current personal verification systems in many security applications. As a matter of fact, the project of any biometric verification system cannot ignore the vulnerability to spoofing or presentation attacks (PAs), which must be addressed by effective countermeasures from the beginning of the design process. However, despite significant improvements, especially by adopting deep learning approaches to fingerprint Presentation Attack Detectors (PADs), current research did not state much about their effectiveness when embedded in fingerprint verification systems. We believe that the lack of works is explained by the lack of instruments to investigate the problem, that is, modelling the cause-effect relationships when two systems (spoof detection and matching) with non-zero error rates are integrated. To solve this lack of investigations in the literature, we present in this PhD thesis a novel performance simulation model based on the probabilistic relationships between the Receiver Operating Characteristics (ROC) of the two systems when implemented sequentially. As a matter of fact, this is the most straightforward, flexible, and widespread approach. We carry out simulations on the PAD algorithms’ ROCs submitted to the editions of LivDet 2017-2019, the NIST Bozorth3, and the top-level VeriFinger 12.0 matchers. With the help of this simulator, the overall system performance can be predicted before actual implementation, thus simplifying the process of setting the best trade-off among error rates. In the second part of this thesis, we exploit this model to define a practical evaluation criterion to assess whether operational points of the PAD exist that do not alter the expected or previous performance given by the verification system alone. Experimental simulations coupled with the theoretical expectations confirm that this trade-off allows a complete view of the sequential embedding potentials worthy of being extended to other integration approaches

Mobile personal authentication using fingerprint.

Cheng Po Sum.Thesis submitted in: July 2003.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 64-67).Abstracts in English and Chinese.List of Figures --- p.iList of Tables --- p.iiiAcknowledgments --- p.iv摘要 --- p.vThesis Abstract --- p.viChapter 1. --- Mobile Commerce --- p.1Chapter 1.1 --- Introduction to Mobile Commerce --- p.1Chapter 1.2 --- Mobile commence payment systems --- p.2Chapter 1.3 --- Security in mobile commerce --- p.5Chapter 2. --- Mobile authentication using Fingerprint --- p.10Chapter 2.1 --- Authentication basics --- p.10Chapter 2.2 --- Fingerprint basics --- p.12Chapter 2.3 --- Fingerprint authentication using mobile device --- p.15Chapter 3. --- Design of Mobile Fingerprint Authentication Device --- p.19Chapter 3.1 --- Objectives --- p.19Chapter 3.2 --- Hardware and software design --- p.21Chapter 3.2.1 --- Choice of hardware platform --- p.21Chapter 3.3 --- Experiments --- p.25Chapter 3.3.1 --- Design methodology I - DSP --- p.25Chapter 3.3.1.1 --- Hardware platform --- p.25Chapter 3.3.1.2 --- Software platform --- p.26Chapter 3.3.1.3 --- Implementation --- p.26Chapter 3.3.1.4 --- Experiment and result --- p.27Chapter 3.3.2 --- Design methodology II ´ؤ SoC --- p.28Chapter 3.3.2.1 --- Hardware components --- p.28Chapter 3.3.2.2 --- Software components --- p.29Chapter 3.3.2.3 --- Implementation Department of Computer Science and Engineering --- p.29Chapter 3.3.2.4 --- Experiment and result --- p.30Chapter 3.4 --- Observation --- p.30Chapter 4. --- Implementation of the Device --- p.31Chapter 4.1 --- Choice of platforms --- p.31Chapter 4.2 --- Implementation Details --- p.31Chapter 4.2.1 --- Hardware implementation --- p.31Chapter 4.2.1.1 --- Atmel FingerChip --- p.32Chapter 4.2.1.2 --- Gemplus smart card and reader --- p.33Chapter 4.2.2 --- Software implementation --- p.33Chapter 4.2.2.1 --- Operating System --- p.33Chapter 4.2.2.2 --- File System --- p.33Chapter 4.2.2.3 --- Device Driver --- p.35Chapter 4.2.2.4 --- Smart card --- p.38Chapter 4.2.2.5 --- Fingerprint software --- p.41Chapter 4.2.2.6 --- Graphical user interface --- p.41Chapter 4.3 --- Results and observations --- p.44Chapter 5. --- An Application Example 一 A Penalty Ticket Payment System (PTPS) --- p.47Chapter 5.1 --- Requirement --- p.47Chapter 5.2 --- Design Principles --- p.48Chapter 5.3 --- Implementation --- p.52Chapter 5.4 --- Results and Observation --- p.57Chapter 6. --- Conclusions and future work --- p.62Chapter 7. --- References --- p.6

Intelligent Feature Selection Techniques for Pattern Classification of Time-Domain Signals

Time-domain signals form the basis of analysis for a variety of applications, including those involving variable conditions or physical changes that result in degraded signal quality. Typical approaches to signal analysis fail under these conditions, as these types of changes often lie outside the scope of the domain\u27s basic analytic theory and are too complex for modeling. Sophisticated signal processing techniques are required as a result. In this work, we develop a robust signal analysis technique that is suitable for a wide variety of time-domain signal analysis applications. Statistical pattern classification routines are applied to problems of interest involving a physical change in the domain of the problem that translate into changes in the signal characteristics. The basis of this technique involves a signal transformation known as the Dynamic Wavelet Fingerprint, used to generate a feature space in addition to features related to the physical domain of the individual application. Feature selection techniques are explored that incorporate the context of the problem into the feature space reduction in an attempt to identify optimal representations of these data sets

Fingerprint-based biometric recognition allied to fuzzy-neural feature classification.

The research investigates fingerprint recognition as one of the most reliable biometrics identification methods. An automatic identification process of humans-based on fingerprints requires the input fingerprint to be matched with a large number of fingerprints in a database. To reduce the search time and computational complexity, it is desirable to classify the database of fingerprints into an accurate and consistent manner so that the input fingerprint is matched only with a subset of the fingerprints in the database. In this regard, the research addressed fingerprint classification. The goal is to improve the accuracy and speed up of existing automatic fingerprint identification algorithms. The investigation is based on analysis of fingerprint characteristics and feature classification using neural network and fuzzy-neural classifiers.The methodology developed, is comprised of image processing, computation of a directional field image, singular-point detection, and feature vector encoding. The statistical distribution of feature vectors was analysed using SPSS. Three types of classifiers, namely, multi-layered perceptrons, radial basis function and fuzzy-neural methods were implemented. The developed classification systems were tested and evaluated on 4,000 fingerprint images on the NIST-4 database. For the five-class problem, classification accuracy of 96.2% for FNN, 96.07% for MLP and 84.54% for RBF was achieved, without any rejection. FNN and MLP classification results are significant in comparison with existing studies, which have been reviewed