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.