The utilisation of biometrics in mobile scenarios is increasing remarkably. At the same time, handwritten signature recognition is one of the modalities with highest potential of use for those applications where customers are used to sign in those traditional processes. However, several improvements have to be made in order to reach acceptable levels of performance, reliability and interoperability. The evaluation carried out in this study contributes with multiple results obtained from 43 users signing 60 times, divided in three sessions, in eight different capture devices, being six of them mobile devices and the other two digitisers specially made for signing and used as a baseline. At each session, a total of 20 signatures per user are captured by each device, so that the evaluation here reported a total of 20 640 signatures, stored in ISO/IEC 19794–7 format. The algorithm applied is a DTW‐based one, particularly modified for mobile environments. The results analysed include inter‐operability, visual feedback and modality tests. One of the big challenges of this research was to discover if the handwritten signature modality in mobile devices should be split into two different modalities, one for those cases when the signature is performed with a stylus, and another when the fingertip is used for signing. Many relevant conclusions have been collected and, over all, multiple improvements have been reached contributing to future deployments of biometrics in mobile environments.

Biometrics has burst into mobile technology. Fingerprint scanners are being embedded in smartphones and tablets supplying these devices with the security and usability provided by biometric authentication mechanisms. However, performance results obtained by biometric systems cannot be extrapolated to mobile devices. The conditions change, especially at capture process, due to the reduced sensing area of the scanners used. The impact of small fingerprint scanners on the quality and biometric performance of the system is studied. A database using three different fingerprint scanners has been collected and reduced‐size images (i.e. 12 × 12 mm ² , 10 × 10 mm ² and 8 × 8 mm ² ) have been modelled by cropping the original ones. Performance testing has been conducted using one public and one commercial algorithm, and considering two application scenarios. One scenario in which enrolment and authentication are executed using the same small sensor included in the mobile device (i.e. cropped image against cropped image) and a second scenario in which enrolment is executed using an external larger sensor and authentication is done using the mobile device sensor (i.e. full image against cropped image). Results show the gradual worsening of quality and error rates as the size of the fingerprint scanner is reduced revealing a significant difference between the application scenarios analysed.

As a consequence of the wide deployment of biometrics-based recognition systems, there are increasing concerns about the security of the sensitive information managed. Various techniques have been proposed in the literature for the biometric templates protection (BTP), having gained great popularity the crypto-biometric systems. In the present paper we propose the implementation of a Fuzzy Vault (FV) scheme based on fixed-length templates with application to dynamic signature verification (DSV), where only 15 global features of the signature are considered to form the templates. The performance of the proposed system is evaluated using three databases: a proprietary collection of signatures, and the publicly available databases MCYT and BioSecure. The experimental results show very similar verification performance compared to an equivalent unprotected system.

In this article, a gait recognition algorithm is presented based on the information obtained from inertial sensors embedded in a smartphone, in particular, the accelerometers and gyroscopes typically embedded on them. The algorithm processes the signal by extracting gait cycles, which are then fed into a Recurrent Neural Network (RNN) to generate feature vectors. To optimize the accuracy of this algorithm, we apply a random grid hyperparameter selection process followed by a hand-tuning method to reach the final hyperparameter configuration. The different configurations are tested on a public database with 744 users and compared with other algorithms that were previously tested on the same database. After reaching the best-performing configuration for our algorithm, we obtain an equal error rate (EER) of 11.48% when training with only 20% of the users. Even better, when using 70% of the users for training, that value drops to 7.55%. The system manages to improve on state-of-the-art methods, but we believe the algorithm could reach a significantly better performance if it was trained with more visits per user. With a large enough database with several visits per user, the algorithm could improve substantially.

Biometric-based recognition has been replacing conventional recognition methods in security systems. Modern electronic devices such as smartphones and online services have been employing biometric systems because of their security, acceptability, and usability. However, the wide deployment of Biometrics raises security concerns including attacks that aim to interfere with a system’s operation. This paper provides a review of potential threats which may affect biometric systems’ security, particularly, Presentation Attack (PA). A general taxonomy of presentation attacks is proposed to cover different biometric modalities considering the attacker’s intention and the presentation instrument. Moreover, Presentation Attack Detection (PAD) mechanisms that aim to eliminate or mitigate those attacks are also taxonomized. The taxonomy analyzes PAD mechanisms wherein the biometric trait pattern is considered to classify PAD methods. A state of the art study has been carried out to investigate PA and PAD for six biological and behavioral modalities.

Handwriting signature is the most diffuse mean for personal identification. Lots of works have been carried out to get reasonable errors rates within automatic signature verification on-line. Most of the algorithms that have been used for matching work by features extraction. This paper deals with the analysis of discriminative powers of the features that can be extracted from an on-line signature, how it's possible to increase those discriminative powers by dynamic time warping as a step in the preprocessing of the signal coming from the tablet. Also it will be covered the influence of this new step in the performance of the Gaussian mixture models algorithm, which has been shown as a successfully algorithm for on-line automatic signature verification in recent studies. A complete experimental evaluation of the algorithm base on dynamic time warping and Gaussian Mixture Models has been conducted on 2500 genuine signatures samples and 2500 skilled forgery samples from 100 users. Those samples are included at the public access MCyT-Signature-Corpus Database.