Fingerprint-based biometric systems have experienced a large development in the past. In spite of many advantages, they are still vulnerable to attack presentations (APs). Therefore, the task of determining whether a sample stems from a live subject (i.e., bona fide) or from an artificial replica is a mandatory requirement which has recently received a considerable attention. Nowadays, when the materials for the fabrication of the Presentation Attack Instruments (PAIs) have been used to train the Presentation Attack Detection (PAD) methods, the PAIs can be successfully identified in most cases. However, current PAD methods still face difficulties detecting PAIs built from unknown materials and/or unknown recepies, or acquired using different capture devices. To tackle this issue, we propose a new PAD technique based on three image representation approaches combining local and global information of the fingerprint. By transforming these representations into a common feature space, we can correctly discriminate bona fide from attack presentations in the aforementioned scenarios. The experimental evaluation of our proposal over the LivDet 2011 to 2019 databases, yielded error rates outperforming the top state-of-the-art results by up to 72% in the most challenging scenarios. In addition, the best representation achieved the best results in the LivDet 2019 competition (overall accuracy of 96.17%).

In past decades, the broad development experienced by biometric systems has unveiled several threats that may decrease their trustworthiness. These are attack presentations that can be easily carried out by a non‐authorised subject to gain access to the biometric system. To mitigate those security concerns, most face presentation attack detection techniques have reported good detection performance when they are evaluated on known presentation attack instruments (PAIs) and under acquisition conditions. In contrast, for more realistic scenarios, existing algorithms face difficulties in detecting unknown PAI species which are only included in the test set. A feature space based on Fisher Vectors computed from compact binarised statistical image features histograms, which allows discovering semantic feature subsets from known samples to enhance the detection of unknown attacks is presented. This representation, which is evaluated for challenging unknown attacks taken from freely available facial databases, reports promising results. A Bona Fide Presentation Classification Error Rate 100 under 17% together with an area under the receiving operating characteristic curve of over 98% can be achieved in the presence of unknown attacks. In addition, by training a limited number of parameters, the method is able to achieve state‐of‐the‐art deep learning‐based approaches for cross‐dataset scenarios.

The development of large-scale identification systems that ensure the privacy protection of enrolled subjects represents a major challenge. Biometric deployments that provide interoperability and usability by including efficient multi-biometric solutions are a recent requirement. In the context of privacy protection, several template protection schemes have been proposed in the past. However, these schemes seem inadequate for indexing (workload reduction) in biometric identification systems. More specifically, they have been used in identification systems that perform exhaustive searches, leading to a degradation of computational efficiency. To overcome these limitations, we present an efficient privacy-preserving multi-biometric identification system that retrieves protected deep cancelable templates and is agnostic with respect to biometric characteristics and biometric template protection schemes. To this end, a multi-biometric binning scheme is designed to exploit the low intra-class variation properties contained in the frequent binary patterns extracted from different types of biometric characteristics. Experimental results reported on publicly available databases using state-of-the-art Deep Neural Network (DNN)-based embedding extractors show that the protected multi-biometric identification system can reduce the computational workload to approximately 57% (indexing up to three types of biometric characteristics) and 53% (indexing up to two types of biometric characteristics), while simultaneously improving the biometric performance of the baseline biometric system at the high-security thresholds. Code is available at https://github.com/dosorior/FBP-Multi-biometric-Indexing .

In spite of the advantages of using fingerprints for subject authentication, several works have shown that fingerprint recognition systems can be easily circumvented by means of artificial fingerprints or presentation attack instruments (PAIs). In order to address that threat, the existing presentation attack detection (PAD) methods have reported a high detection performance when materials used for the fabrication of PAIs and capture devices are known. However, for more complex and realistic scenarios where one of those factors remains unknown, these PAD methods are unable to correctly separate a PAI from a real fingerprint (i.e. bona fide presentation). In this article, a new PAD approach based on the Fisher Vector technique, which combines local and global information of several local feature descriptors in order to improve the PAD generalisation capabilities, was proposed. The experimental results over unknown scenarios taken from LivDet 2011 to LivDet 2017 show that our proposal reduces the top state‐of‐the‐art average classification error rates by up to four times, thereby making it suitable in real applications demanding high security. In addition, the best single configuration achieved the best results in the LivDet 2019 competition, with an overall accuracy of 96.17%.

Presentation Attack Detection (PAD) is the task of determining whether a sample stems from a live subject (bona fide presentation) or from an artificial replica (Presentation Attack Instrument, PAI). Several PAD approaches have shown high effectiveness to successfully detect PAIs when the materials used for the fabrication of these PAIs are known a priori. However, most of these PAD methods do not take into account the characteristics of PAIs’ species in order to generalise to new, realistic and more challenging scenarios, where materials might be unknown. Based on that fact, in this work, we explore the impact of different PAI species, fabricated with different materials, on several local-based descriptors combined with the Fisher Vector feature encoding, in order to increase the robustness to unknown attacks. The experimental results over the well-established benchmarks of the LivDet 2011, LivDet 2013 and LivDet 2015 competitions reported error rates outperforming the top state-of-the-art in the presence of unknown attacks. Moreover, the evaluation revealed the differences in the detection performance due to the variability between the PAI species.

In many forensic scenarios, criminals often attempt to conceal their identity by covering their face and other distinctive body parts. In such situations, physical evidence may, however, reveal other unique characteristics, e.g. hands, which can be used to identify offenders. In this context, several state-of-the-art biometric recognition systems have been proposed recently. These recognition systems offer high identification performance in restricted environments. However, in forensic scenarios, the environment is often unconstrained, making biometric identification considerably more difficult, with a consequent decrease in accuracy. In this article, we explore methods (e.g. hand alignment and information fusion) to improve the identification of subjects within forensic investigations. Experimental results show that explored techniques play an important role in the improvement of the identification performance of existing schemes: the combination of hand alignment and information fusion results in the highest Rank-1 identification performance improvement of up to 13.10% (i.e., 26.30% vs. 13.20%) and 16.30% (i.e., 77.00% vs. 60.70%) with respect to the baseline for the unconstrained databases NTU-PI_v1 and HaGRID, respectively ( https://github.com/ljsoler/IF-HA-HandRecognition ).