Feature Fusion for Fingerprint Liveness Detection

For decades, fingerprints have been the most widely used biometric trait in identity recognition systems, thanks to their natural uniqueness, even in rare cases such as identical twins. Recently, we witnessed a growth in the use of fingerprint-based recognition systems in a large variety of devices and applications. This, as a consequence, increased the benefits for offenders capable of attacking these systems. One of the main issues with the current fingerprint authentication systems is that, even though they are quite accurate in terms of identity verification, they can be easily spoofed by presenting to the input sensor an artificial replica of the fingertip skin’s ridge-valley patterns. Due to the criticality of this threat, it is crucial to develop countermeasure methods capable of facing and preventing these kind of attacks. The most effective counter–spoofing methods are those trying to distinguish between a "live" and a "fake" fingerprint before it is actually submitted to the recognition system. According to the technology used, these methods are mainly divided into hardware and software-based systems. Hardware-based methods rely on extra sensors to gain more pieces of information regarding the vitality of the fingerprint owner. On the contrary, software-based methods merely rely on analyzing the fingerprint images acquired by the scanner. Software-based methods can then be further divided into dynamic, aimed at analyzing sequences of images to capture those vital signs typical of a real fingerprint, and static, which process a single fingerprint impression. Among these different approaches, static software-based methods come with three main benefits. First, they are cheaper, since they do not require the deployment of any additional sensor to perform liveness detection. Second, they are faster since the information they require is extracted from the same input image acquired for the identification task. Third, they are potentially capable of tackling novel forms of attack through an update of the software. The interest in this type of counter–spoofing methods is at the basis of this dissertation, which addresses the fingerprint liveness detection under a peculiar perspective, which stems from the following consideration. Generally speaking, this problem has been tackled in the literature with many different approaches. Most of them are based on first identifying the most suitable image features for the problem in analysis and, then, into developing some classification system based on them. In particular, most of the published methods rely on a single type of feature to perform this task. Each of this individual features can be more or less discriminative and often highlights some peculiar characteristics of the data in analysis, often complementary with that of other feature. Thus, one possible idea to improve the classification accuracy is to find effective ways to combine them, in order to mutually exploit their individual strengths and soften, at the same time, their weakness. However, such a "multi-view" approach has been relatively overlooked in the literature. Based on the latter observation, the first part of this work attempts to investigate proper feature fusion methods capable of improving the generalization and robustness of fingerprint liveness detection systems and enhance their classification strength. Then, in the second part, it approaches the feature fusion method in a different way, that is by first dividing the fingerprint image into smaller parts, then extracting an evidence about the liveness of each of these patches and, finally, combining all these pieces of information in order to take the final classification decision. The different approaches have been thoroughly analyzed and assessed by comparing their results (on a large number of datasets and using the same experimental protocol) with that of other works in the literature. The experimental results discussed in this dissertation show that the proposed approaches are capable of obtaining state–of–the–art results, thus demonstrating their effectiveness

GAINE - A Portable Framework for the Development of Edutainment Applications Based on Multitouch and Tangible Interaction

In the last few years, Multitouch and Tangible User Interfaces have emerged as a powerful tool to integrate interactive surfaces and responsive spaces that embody digital information. Besides providing a natural interaction with digital contents, they allow the interaction of multiple users at the same time, thus promoting collaborative activities and information sharing. In particular, these characteristics have opened new exploration possibilities in the edutainment context, as witnessed by the many applications successfully developed in different areas, from children’s collaborative learning to interactive storytelling, cultural heritage and medical therapy support. However, due to the availability of different multitouch and tangible interaction technologies and of different target computing platforms, the development and deployment of such applications can be challenging. To this end, in this paper we present GAINE (tanGible Augmented INteraction for Edutainment), a software framework that enables rapid prototyping and development of tangible augmented applications for edutainment purposes. GAINE has two main features. First, it offers developers high-level context specific constructs that significantly reduces the implementation burden. Second, the framework is portable on different operating systems and offers independence from the underlying hardware and tracking technology. In this paper, we also discuss several case studies to show the effectiveness of GAINE in simplifying the development of entertainment and edutainment applications based on multitouch and tangible interaction

CNN Patch--Based Voting for Fingerprint Liveness Detection

Biometric identification systems based on fingerprints are vulnerable to attacks that use fake replicas of real fingerprints. One possible countermeasure to this issue consists in developing software modules capable of telling the liveness of an input image and, thus, of discarding fakes prior to the recognition step. This paper presents a fingerprint liveness detection method founded on a patch-based voting approach. Fingerprint images are first segmented to discard background information. Then, small-sized foreground patches are extracted and processed by a well-know Convolutional Neural Network model adapted to the problem at hand. Finally, the patch scores are combined to draw the final fingerprint label. Experimental results on well-established benchmarks demonstrate a promising performance of the proposed method compared with several state-of-the-art algorithms

Virtual reality haptic simulation of root canal therapy

Here we report a haptic virtual reality simulator for root canal treatment (endodontic procedures). A virtual jaw model was extracted from CT data of a live patient and the volumetric data obtained were visualized using a Marching Cubes algorithm. Collision detection and collision response algorithms were developed using a voxel-based approach. Removal of bone was visualized using a modified real-time Marching Cubes method and deformation of the K-files in the internal surface of tooth canal was simulated haptically and graphically using OpenGL and HDAPI of OpenHaptics Libraries. Using a haptic robot (Omni Phantom) the user can burr the enamel and dentin until reaching the pulp chamber and then the internal surface of a root canal can be cleaned using a simulated K-file

GAINE - tanGible Augmented INteraction for Edutainment

Interactive tabletops are gaining an increasing interest since they provide a more natural interaction with digital contents and allow the interaction of multiple users at a time promoting face-to-face collaboration, information sharing and the raise of social experiences. Given the potentialities offered by these devices, several entertainment-edutainment applications based on interactive tabletops have been successfully developed in different areas, from medical therapy support to children's collaborative learning, interactive storytelling and cultural heritage. However, the development of such applications often requires complex technical and implementation skills. Taking this into consideration, in this paper we present GAINE (tanGible Augmented INteraction for Edutainment), a software framework aimed at the rapid prototyping and development of interactive tabletop games. GAINE offers developers context specific high-level constructs and a simple scripting language that simplifies the implementation task. The framework is portable on different operating systems and offers independence from the underlying hardware. Two practical case studies are thoroughly discussed to show how GAINE can simplify the development of interactive tabletop applications in the entertainment and edutainment context

Feature Fusion for Fingerprint Liveness Detection: A Comparative Study

Spoofing attacks carried out using artificial replicas are a severe threat for fingerprint-based biometric systems and, thus, require the development of effective countermeasures. One possible protection method is to implement software modules that analyze fingerprint images to tell live from fake samples. Most of the static software-based approaches in the literature are based on various image features, each with its own strengths, weaknesses, and discriminative power. Such features can be seen as different and often complementary views of the object in analysis, and their fusion is likely to improve the classification accuracy. This paper aims at assessing the potential of these feature fusion approaches in the area of fingerprint liveness detection by analyzing different features and different methods for their aggregation. Experiments on publicly available benchmarks show the effectiveness of feature fusion methods, which improve the accuracy of those based on individual features and are competitive with respect to the alternative methods, such as the ones based on convolutional neural networks.Fil: Toosi, Amirhosein. Politecnico di Torino; ItaliaFil: Bottino, Andrea. Politecnico di Torino; ItaliaFil: Cumani, Sandro. Politecnico di Torino; ItaliaFil: Negri, Pablo Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; ArgentinaFil: Sottile, Pietro Luca. Politecnico di Torino; Itali

Generative Adversarial Networks for Anomaly Detection in Biomedical Imaging: A Study on Seven Medical Image Datasets

Anomaly detection (AD) is a challenging problem in computer vision. Particularly in the field of medical imaging, AD poses even more challenges due to a number of reasons, including insufficient availability of ground truth (annotated) data. In recent years, AD models based on generative adversarial networks (GANs) have made significant progress. However, their effectiveness in biomedical imaging remains underexplored. In this paper, we present an overview of using GANs for AD, as well as an investigation of state-of-the-art GAN-based AD methods for biomedical imaging and the challenges encountered in detail. We have also specifically investigated the advantages and limitations of AD methods on medical image datasets, conducting experiments using 3 AD methods on 7 medical imaging datasets from different modalities and organs/tissues. Given the highly different findings achieved across these experiments, we further analyzed the results from both data-centric and model-centric points of view. The results showed that none of the methods had a reliable performance for detecting abnormalities in medical images. Factors such as the number of training samples, the subtlety of the anomaly, and the dispersion of the anomaly in the images are among the phenomena that highly impact the performance of the AD models. The obtained results were highly variable (AUC: 0.475-0.991; Sensitivity: 0.17-0.98; Specificity: 0.14-0.97). In addition, we provide recommendations for the deployment of AD models in medical imaging and foresee important research directions