LivDet in Action - Fingerprint Liveness Detection Competition 2019

The International Fingerprint liveness Detection Competition (LivDet) is an open and well-acknowledged meeting point of academies and private companies that deal with the problem of distinguishing images coming from reproductions of fingerprints made of artificial materials and images relative to real fingerprints. In this edition of LivDet we invited the competitors to propose integrated algorithms with matching systems. The goal was to investigate at which extent this integration impact on the whole performance. Twelve algorithms were submitted to the competition, eight of which worked on integrated systems.Comment: Preprint version of a paper accepted at ICB 201

LivDet 2017 Fingerprint Liveness Detection Competition 2017

Fingerprint Presentation Attack Detection (FPAD) deals with distinguishing images coming from artificial replicas of the fingerprint characteristic, made up of materials like silicone, gelatine or latex, and images coming from alive fingerprints. Images are captured by modern scanners, typically relying on solid-state or optical technologies. Since from 2009, the Fingerprint Liveness Detection Competition (LivDet) aims to assess the performance of the state-of-the-art algorithms according to a rigorous experimental protocol and, at the same time, a simple overview of the basic achievements. The competition is open to all academics research centers and all companies that work in this field. The positive, increasing trend of the participants number, which supports the success of this initiative, is confirmed even this year: 17 algorithms were submitted to the competition, with a larger involvement of companies and academies. This means that the topic is relevant for both sides, and points out that a lot of work must be done in terms of fundamental and applied research.Comment: presented at ICB 201

On the interoperability of capture devices in fingerprint presentation attacks detection

Abstract A presentation attack consists in submitting to the fingerprint capture device an artificial replica of the finger of the targeted client. If the sensor is not equipped with an appropriate algorithm aimed to detect the fingerprint spoof, the system processes the obtained image as a one belonging to a real fingerprint. In order to face this problem, several presentation attacks detection (PAD) algorithms have been proposed so far. Current methods heavily rely on features extracted from a large data set of fake and real fingerprint images, and an appropriate classifier trained with such data to distinguish between live (real) and fake (spoof) fingerprint images. Building such data set requires a significant effort for fabricating samples of fake fingerprints, with the most effective materials used to circumvent the sensor. Interesting and promising results have been obtained, but they also suggest that the PAD is tailored on the particular sensor. Small and significant differences also occur when a novel version of the same sensor is released, and this may affect the PAD. Therefore, making a PAD interoperable is among the main current issues when considering fingerprints as the first level of protection and security of logical or physical resources. This paper is a first attempt to assess at which extent the sensor interoperability can be an issue for fingerprint PADs and to eventually propose a solution to this limitation. In particular, textural features will be under focus and a feature space transformation method based on the least square is proposed

On the interoperability of capture devices in fingerprint presentation attacks detection

A presentation attack consists in submitting to the fingerprint capture device an artificial replica of the finger of the targeted client. If the sensor is not equipped with an appropriate algorithm aimed to detect the fingerprint spoof, the system processes the obtained image as a one belonging to a real fingerprint. In order to face this problem, several presentation attacks detection (PAD) algorithms have been proposed so far. Current methods heavily rely on features extracted from a large data set of fake and real fingerprint images, and an appropriate classifier trained with such data to distinguish between live (real) and fake (spoof) fingerprint images. Building such data set requires a significant effort for fabricating samples of fake fingerprints, with the most effective materials used to circumvent the sensor. Interesting and promising results have been obtained, but they also suggest that the PAD is tailored on the particular sensor. Small and significant differences also occur when a novel version of the same sensor is released, and this may affect the PAD. Therefore, making a PAD interoperable is among the main current issues when considering fingerprints as the first level of protection and security of logical or physical resources. This paper is a first attempt to assess at which extent the sensor interoperability can be an issue for fingerprint PADs and to eventually propose a solution to this limitation. In particular, textural features will be under focus and a feature space transformation method based on the least square is proposed

A classification-selection approach for self updating of face verification systems under stringent storage and computational requirements

Nowadays face recognition systems have many application fields. Unfortunately, lighting variations and ageing effects are still open issues. Moreover, face changes over time due to ageing. A further problem is due to occlusions, for example the glass presence. Re-enrolling user’s face is time-consuming and does not solve above problems. Therefore, unsupervised template update has been proposed, and named self update. Basically, this algorithm adapts/modifies templates or face models by collecting samples during system operations. The most effective variant of self update is based on the collection of multiple templates. However, this approach has been evaluated and tested in conditions under which the possible number of collectable templates is uncostrained. Actually, available resources are limited in memory and computational power, thus it is likely that it is not possible to have more than a pre-set number of templates. In this paper, we propose a classification-selection approach, based on the combination of self update and C-means algorithms, which keeps constant the number of templates and improve the ratio between intra-class variations and inter-class variations for each user. Experimental results show the effectiveness of this method with respect to standard self update

User-specific effects in Fingerprint Presentation Attacks Detection: Insights for future research

A fingerprint presentation attacks detector (FPAD) is designed to obtain a certain performance regardless of the targeted user population. However, two recent works on facial traits showed that a PAD system can exploit very useful information from the targeted user population. In this paper, we explored the existence of that kind of information in fingerprints when textural features are adopted. We show by experiments that such features embed not only intrinsic differences of the given fingerprint replica with respect to a generic live fingerprint, but also contains characteristics present in other fingers of the same user, and characteristics extracted directly from spoofs of the targeted fingerprint itself. These interesting evidences could lead to novel developments in the design of future FPADs

Improved human gait recognition

Gait recognition is an emerging biometric technology which aims to identify people purely through the analysis of the way they walk. The technology has attracted interest as a method of identification because of its non-invasiveness, since it does not require the subject’s cooperation. However, "covariates" which include clothing, carrying conditions, and other intra-class variations affect the recognition performances. This paper proposes a feature selection mask which is able to select most relevant discriminative features for human recognition to alleviate the impact of covariates so as to improve the recognition performances. The proposed method has been evaluated using CASIA Gait Database (Dataset B) and the experimental results demonstrate that the proposed technique yields 77.38 % of correct recognitio

On combining edge detection methods for improving BSIF based facial recognition performances

Lighting variation is a major challenge for an automatic face recognition system. In order to overcome this problem, many methods have been proposed. Most of them try to extract features invariant to illumination changes or to reduce illumination changes in a pre-processing step and to extract features for recognition. In this paper, we present a procedure similar to the latter where the two steps are complementary. In the pre-processing step we deal with the illumination changes and in the features extraction step we use the BSIF (Binarized Statistical Image Features), a recently proposed textural algorithm. In our opinion, a method capable of reducing the lighting variations is ideal for an algorithm like the BSIF. The performance of our system has been tested on the FRGC dataset and the presented results show the validity of our approach

Upregulation of p75NTR by Histone Deacetylase Inhibitors Sensitizes Human Neuroblastoma Cells to Targeted Immunotoxin-Induced Apoptosis

Histone deacetylase (HDAC) inhibitors are novel chemotherapy agents with potential utility in the treatment of neuroblastoma, the most frequent solid tumor of childhood. Previous studies have shown that the exposure of human neuroblastoma cells to some HDAC inhibitors enhanced the expression of the common neurotrophin receptor p75NTR. In the present study we investigated whether the upregulation of p75NTR could be exploited to render neuroblastoma cells susceptible to the cytotoxic action of an anti-p75NTR antibody conjugated to the toxin saporin-S6 (p75IgG-Sap). We found that two well-characterized HDAC inhibitors, valproic acid (VPA) and entinostat, were able to induce a strong expression of p75NTR in different human neuroblastoma cell lines but not in other cells, with entinostat, displaying a greater efficacy than VPA. Cell pretreatment with entinostat enhanced p75NTR internalization and intracellular saporin-S6 delivery following p75IgG-Sap exposure. The addition of p75IgG-Sap had no effect on vehicle-pretreated cells but potentiated the apoptotic cell death that was induced by entinostat. In three-dimensional neuroblastoma cell cultures, the subsequent treatment with p75IgG-Sap enhanced the inhibition of spheroid growth and the impairment of cell viability that was produced by entinostat. In athymic mice bearing neuroblastoma xenografts, chronic treatment with entinostat increased the expression of p75NTR in tumors but not in liver, kidney, heart, and cerebellum. The administration of p75IgG-Sap induced apoptosis only in tumors of mice that were pretreated with entinostat. These findings define a novel experimental strategy to selectively eliminate neuroblastoma cells based on the sequential treatment with entinostat and a toxin-conjugated anti-p75NTR antibody