Investigating the impact of combining handwritten signature and keyboard keystroke dynamics for gender prediction

© 2019 IEEE. The use of soft-biometric data as an auxiliary tool on user identification is already well known. Gender, handorientation and emotional state are some examples which can be called soft-biometrics. These soft-biometric data can be predicted directly from the biometric templates. It is very common to find researches using physiological modalities for soft-biometric prediction, but behavioural biometric is often not well explored for this context. Among the behavioural biometric modalities, keystroke dynamics and handwriting signature have been widely explored for user identification, including some soft-biometric predictions. However, in these modalities, the soft-biometric prediction is usually done in an individual way. In order to fill this space, this study aims to investigate whether the combination of those two biometric modalities can impact the performance of a soft-biometric data, gender prediction. The main aim is to assess the impact of combining data from two different biometric sources in gender prediction. Our findings indicated gains in terms of performance for gender prediction when combining these two biometric modalities, when compared to the individual ones

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

An empirical biometric-based study for user identification from different roles in the online game League of Legends

© 2017 CEUR-WS. All rights reserved. The popularity of computer games has grown exponentially in the last few years. In some games, players can choose to play with different characters from a pre-defined list, exercising distinct roles in each match. Although such games were created to promote competition and promote self-improvement, there are several recurrent issues. One that has received the least amount of attention is the problem of "account sharing" so far is when a player pays more experienced players to progressing in the game. The companies running those games tend to punish this behaviour, but this specific case is hard to identify. The aim of this study is to use a database of mouse and keystroke dynamics biometric data of League of Legends players as a case study to understand the specific characteristics a player will keep (or not) when playing different roles and distinct characters

An evaluation of a three-modal hand-based database to forensic-based gender recognition

In recent years, behavioural soft-biometrics have been widely used to improve biometric systems performance. Information like gender, age and ethnicity can be obtained from more than one behavioural modality. In this paper, we propose a multimodal hand-based behavioural database for gender recognition. Thus, our goal in this paper is to evaluate the performance of the multimodal database. For this, the experiment was realised with 76 users and was collected keyboard dynamics, touchscreen dynamics and handwritten signature data. Our approach consists of compare two-modal and one-modal modalities of the biometric data with the multimodal database. Traditional and new classifiers were used and the statistical Kruskal-Wallis to analyse the accuracy of the databases. The results showed that the multimodal database outperforms the other databases

Web-Based Benchmark for Keystroke Dynamics Biometric Systems: A Statistical Analysis

Most keystroke dynamics studies have been evaluated using a specific kind of dataset in which users type an imposed login and password. Moreover, these studies are optimistics since most of them use different acquisition protocols, private datasets, controlled environment, etc. In order to enhance the accuracy of keystroke dynamics' performance, the main contribution of this paper is twofold. First, we provide a new kind of dataset in which users have typed both an imposed and a chosen pairs of logins and passwords. In addition, the keystroke dynamics samples are collected in a web-based uncontrolled environment (OS, keyboards, browser, etc.). Such kind of dataset is important since it provides us more realistic results of keystroke dynamics' performance in comparison to the literature (controlled environment, etc.). Second, we present a statistical analysis of well known assertions such as the relationship between performance and password size, impact of fusion schemes on system overall performance, and others such as the relationship between performance and entropy. We put into obviousness in this paper some new results on keystroke dynamics in realistic conditions.Comment: The Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP 2012), Piraeus : Greece (2012

Assentication: User Deauthentication and Lunchtime Attack Mitigation with Seated Posture Biometric

Biometric techniques are often used as an extra security factor in authenticating human users. Numerous biometrics have been proposed and evaluated, each with its own set of benefits and pitfalls. Static biometrics (such as fingerprints) are geared for discrete operation, to identify users, which typically involves some user burden. Meanwhile, behavioral biometrics (such as keystroke dynamics) are well suited for continuous, and sometimes more unobtrusive, operation. One important application domain for biometrics is deauthentication, a means of quickly detecting absence of a previously authenticated user and immediately terminating that user's active secure sessions. Deauthentication is crucial for mitigating so called Lunchtime Attacks, whereby an insider adversary takes over (before any inactivity timeout kicks in) authenticated state of a careless user who walks away from her computer. Motivated primarily by the need for an unobtrusive and continuous biometric to support effective deauthentication, we introduce PoPa, a new hybrid biometric based on a human user's seated posture pattern. PoPa captures a unique combination of physiological and behavioral traits. We describe a low cost fully functioning prototype that involves an office chair instrumented with 16 tiny pressure sensors. We also explore (via user experiments) how PoPa can be used in a typical workplace to provide continuous authentication (and deauthentication) of users. We experimentally assess viability of PoPa in terms of uniqueness by collecting and evaluating posture patterns of a cohort of users. Results show that PoPa exhibits very low false positive, and even lower false negative, rates. In particular, users can be identified with, on average, 91.0% accuracy. Finally, we compare pros and cons of PoPa with those of several prominent biometric based deauthentication techniques

Genetic Programming for Multibiometrics

Biometric systems suffer from some drawbacks: a biometric system can provide in general good performances except with some individuals as its performance depends highly on the quality of the capture. One solution to solve some of these problems is to use multibiometrics where different biometric systems are combined together (multiple captures of the same biometric modality, multiple feature extraction algorithms, multiple biometric modalities...). In this paper, we are interested in score level fusion functions application (i.e., we use a multibiometric authentication scheme which accept or deny the claimant for using an application). In the state of the art, the weighted sum of scores (which is a linear classifier) and the use of an SVM (which is a non linear classifier) provided by different biometric systems provide one of the best performances. We present a new method based on the use of genetic programming giving similar or better performances (depending on the complexity of the database). We derive a score fusion function by assembling some classical primitives functions (+, *, -, ...). We have validated the proposed method on three significant biometric benchmark datasets from the state of the art