Dr Luisa Verdoliva from Università di Napoli Federico II, Italy, talks about the research behind the Letter ‘Wavelet-Markov local descriptor for detecting fake fingerprints’ on page 439. Dr Luisa Verdoliva My research field is image processing and computer vision. Although images convey a huge amount of information, human beings are able to process them in real time and effectively extract useful information. I have always been fascinated by this ability which is what led me to work in this area, in particular on image compression, segmentation and denoising. A few years ago, Carlo Sansone, who is an expert in pattern recognition, presented me with the problem of fingerprint liveness detection. We started to collaborate, along with the other co-authors, and began to obtain encouraging results. Fingerprint-based biometric systems have become increasingly popular and are commonly used for authentication in various security applications (recently also to access smartphones, e.g. iPhone 5s). Relying on features that are unique to each individual, they avoid the problems typical of systems based on the use of passwords. Unfortunately, it is possible to spoof a variety of fingerprint-based systems by reproducing fake fingers on simple moulds made of silicone, play-dough, clay or gelatin. It is important to improve the security of these systems using software-based approaches which are economic, non-invasive and flexible. Telling apart live from fake fingerprints is a binary classification problem. The main ingredient for success is to extract some discriminative (and compact) features from the image, on which a classifier can base its decision. For example, if live fingerprints were crisp and fake fingerprints were blurred, a reliable measure of blurriness would make a very good feature. However, current fakes are very realistic and we must look for much finer differences. The features we propose are based on the joint statistical analysis of wavelet-domain coefficients. They try to capture the behaviour observed locally in very small areas of the image by means of histograms collected over all of these areas. Extensive tests on the databases used in this field show that the local descriptor considered in our work guarantees a very good detection performance, in most cases better than competing techniques typically based on global features. Even if they try to exploit the specific characteristics of the fingerprint pattern (the ridge-valley structure), they are based on macroscopic features that have been shown to be less discriminative than microscopic ones. This work provides some new insights into which features can best capture the differences between live and fake fingerprints. Some existing authentication devices are already equipped with a liveness detection system. Given the simplicity and low complexity of our proposed approach, it can be readily implemented in real-world systems, improving their performance. Using such technologies in scenarios like frontiers and airports is challenging. The number of subjects requiring identification would be so large that even a very low error rate, like that reported in this work, would lead to a huge number of controls. Local descriptors can be used for many important tasks. Besides fingerprint liveness detection, we are using them for iris liveness detection and image forgery detection. We have also recently worked on sensor noise-based forgery localisation. Moreover, I am continuing to work on more traditional image processing problems, such as the compression, denoising and segmentation of remote sensing images, both optical and SAR. Like many problems involving security, this is a two-party game with attacker and defender evolving rapidly; innovation is key to keep the lead. Today's state-of-the-art techniques could not be forecast just ten years ago and we have little idea of what will be the major ideas ten years from now. Fake fingerprints may become much more difficult to detect. The use of fingerprints for authentication, though well established and accepted, will probably lose ground to new and more secure approaches, maybe based on a combination of biometrics.
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Open Access
Dec 1, 2019
Hangyu Gong + 1
