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

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

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

On the Inference of Soft Biometrics from Typing Patterns Collected in a Multi-device Environment

In this paper, we study the inference of gender, major/minor (computer science, non-computer science), typing style, age, and height from the typing patterns collected from 117 individuals in a multi-device environment. The inference of the first three identifiers was considered as classification tasks, while the rest as regression tasks. For classification tasks, we benchmark the performance of six classical machine learning (ML) and four deep learning (DL) classifiers. On the other hand, for regression tasks, we evaluated three ML and four DL-based regressors. The overall experiment consisted of two text-entry (free and fixed) and four device (Desktop, Tablet, Phone, and Combined) configurations. The best arrangements achieved accuracies of 96.15%, 93.02%, and 87.80% for typing style, gender, and major/minor, respectively, and mean absolute errors of 1.77 years and 2.65 inches for age and height, respectively. The results are promising considering the variety of application scenarios that we have listed in this work.Comment: The first two authors contributed equally. The code is available upon request. Please contact the last autho

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

Language Independent Gender Identification Through Keystroke Analysis

Purpose – In this work we investigate the feasibility of iden tifying the gender of an author by measuring the keystroke duration when typing a message. Design/methodology/approach – Three classifiers were constructed and tested. We empirically evaluated the effectiveness of the classifiers by using empirical data. We used primary data as well as a publicly available dataset containing keystrokes from a diff erent language to validate the language independence assumption. Findings – The results of this work indicate that it is possible to identify the gender of an author by analyzing keystroke durations with a probability of success in the region of 70%. Research limitations/implications – The proposed approach was validated with a limited number of participants and languages, yet the statistical tests show the significance of the results. However, t his approach will be further tested with other languages. Practical implications – Having the ability to identify the gender of an aut hor of a certain piece of text has value in digital forensics, as the proposed method will be a source of circumstantial evidence for “putting fingers on keyboard” and for arbitrating cases where the true origin of a message needs to be identified. Social implications – If the proposed method is included as part of a text composing system (such as email, and instant messaging applications) it could increase trust toward the applications that use it and may also work as a deterrent for crimes involving forgery. Originality/value – The proposed approach combines and adapts techniques from the domains of biometric authentication and data classification