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

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

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

Adversarial Activity Detection and Prediction Using Behavioral Biometrics

Behavioral biometrics can be used in different security applications like authentication, identification, etc. One of the trending applications is predicting future activities of people and guessing whether they will engage in malicious activities in the future. In this research, we study the possibility of predicting future activities and propose novel methods for near-future activity prediction. First, we study gait signals captured using smartphone accelerometer sensor and build a model to predict a future gait signal. Activity recognition using body movements captured from mobile phone sensors has been a major point of interest in recent research. Data that is being continuously read from mobile sensors can be used to recognize user activity. We propose a model for predicting human body movements based on the previous activity that has been read from sensors and continuously updating our prediction as new data becomes available. Our results show that our model can predict the future movement signal with a high accuracy that can contribute to several applications in the area. Second, we study keystroke acoustics and build a model for predicting future activities of the users by recording their keystrokes audio. Using keystroke acoustics to predict typed text has significant advantages, such as being recorded covertly from a distance and requiring no physical access to the computer system. Recently, some studies have been done on keystroke acoustics, however, to the best of our knowledge none have used them to predict adversarial activities. On a dataset of two million keystrokes consisting of seven adversarial and one benign activity, we use a signal processing approach to extract keystrokes from the audio and a clustering method to recover the typed letters followed by a text recovery module to regenerate the typed words. Furthermore, we use a neural network model to classify the benign and adversarial activities and achieve significant results: (1) we extract individual keystroke sounds from the raw audio with 91% accuracy and recover words from audio recordings in a noisy environment with 71% average top-10 accuracy. (2) We classify adversarial activities with 93% to 98% average accuracy under different operating scenarios. Third, we study the correlation between the personality traits of users with their keystroke and mouse dynamics. Even with the availability of multiple interfaces, such as voice, touch, etc., keyboard and mouse remain the primary interfaces to a computer. Any insights on the relation between keyboard and mouse dynamics with the personality type of the users can provide foundations for various applications, such as advertisement, social media, etc. We use a dataset of keystroke and mouse dynamics collected from 104 users together with their responses to two personality tests to analyze how their interaction with the computer relates to their personality. Our findings show that there are considerable trends and patterns in keystroke and mouse dynamics that are correlated with each personality type

HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

We introduce hand movement, orientation, and grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data were collected under two conditions: 1) sitting and 2) walking. We achieved authentication equal error rates (EERs) as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved the EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at a 16-Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones)

User Attribution Through Keystroke Dynamics-Based Author Age Estimation

Keystroke dynamics analysis has often been used in user authentication. In this work, it is used to classify users according to their age. The authors have extended their previous research in which they managed to identify the age group that a user belongs to with an accuracy of 66.1%. The main changes made were the use of a larger dataset, which resulted from a new volunteer recording phase, the exploitation of more keystroke dynamics features, and the use of a procedure for selecting those features that can best distinguish users according to their age. Five machine learning models were used for the classification, and their performance in relation to the number of features involved was tested. As a result of these changes in the research method, an improvement in the performance of the proposed system has been achieved. The accuracy of the improved system is 89.7%

Utilizing Linguistic Context To Improve Individual and Cohort Identification in Typed Text

The process of producing written text is complex and constrained by pressures that range from physical to psychological. In a series of three sets of experiments, this thesis demonstrates the effects of linguistic context on the timing patterns of the production of keystrokes. We elucidate the effect of linguistic context at three different levels of granularity: The first set of experiments illustrate how the nontraditional syntax of a single linguistic construct, the multi-word expression, can create significant changes in keystroke production patterns. This set of experiments is followed by a set of experiments that test the hypothesis on the entire linguistic output of an individual. By taking into account linguistic context, we are able to create more informative feature-sets, and utilize these to improve the accuracy of keystroke dynamic-based user authentication. Finally, we extend our findings to entire populations, or demographic cohorts. We show that typing patterns can be used to predict a group\u27s gender, native language and dominant hand. In addition, keystroke patterns can shed light on the cognitive complexity of a task that a typist is engaged in. The findings of these experiments have far-reaching implications for linguists, cognitive scientists, computer security researchers and social scientists

Spotting Fake Profiles in Social Networks via Keystroke Dynamics

Spotting and removing fake profiles could curb the menace of fake news in society. This paper, thus, investigates fake profile detection in social networks via users' typing patterns. We created a novel dataset of 468 posts from 26 users on three social networks: Facebook, Instagram, and X (previously Twitter) over six sessions. Then, we extract a series of features from keystroke timings and use them to predict whether two posts originated from the same users using three prominent statistical methods and their score-level fusion. The models' performance is evaluated under same, cross, and combined-cross-platform scenarios. We report the performance using k-rank accuracy for k varying from 1 to 5. The best-performing model obtained accuracies between 91.6-100% on Facebook (Fusion), 70.8-87.5% on Instagram (Fusion), and 75-87.5% on X (Fusion) for k from 1 to 5. Under a cross-platform scenario, the fusion model achieved mean accuracies of 79.1-91.6%, 87.5-91.6%, and 83.3-87.5% when trained on Facebook, Instagram, and Twitter posts, respectively. In combined cross-platform, which involved mixing two platforms' data for model training while testing happened on the third platform's data, the best model achieved accuracy ranges of 75-95.8% across different scenarios. The results highlight the potential of the presented method in uncovering fake profiles across social network platforms.Comment: 2024 IEEE 21st Consumer Communications \& Networking Conference (CCNC) | 9 pages, 8 figures, 3 algos

Extending the Predictive Capabilities of Hand-oriented Behavioural Biometric Systems

The discipline of biometrics may be broadly defined as the study of using metrics related to human characteristics as a basis for individual identification and authentication, and many approaches have been implemented in recent years for many different scenarios. A sub-section of biometrics, specifically known as soft biometrics, has also been developing rapidly, which focuses on the additional use of information which is characteristic of a user but not unique to one person, examples including subject age or gender. Other than its established value in identification and authentication tasks, such useful user information can also be predicted within soft biometrics modalities. Furthermore, some most recent investigations have demonstrated a demand for utilising these biometric modalities to extract even higher-level user information, such as a subject\textsc{\char13}s mental or emotional state. The study reported in this thesis will focus on investigating two soft biometrics modalities, namely keystroke dynamics and handwriting biometrics (both examples of hand-based biometrics, but with differing characteristics). The study primarily investigates the extent to which these modalities can be used to predict human emotions. A rigorously designed data capture protocol is described and a large and entirely new database is thereby collected, significantly expanding the scale of the databases available for this type of study compared to those reported in the literature. A systematic study of the predictive performance achievable using the data acquired is presented. The core analysis of this study, which is to further explore of the predictive capability of both handwriting and keystroke data, confirm that both modalities have the capability for predicting higher level mental states of individuals. This study also presents the implementation of detailed experiments to investigate in detail some key issues (such as amount of data available, availability of different feature types, and the way ground truth labelling is established) which can enhance the robustness of this higher level state prediction technique