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

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

Enhancing Usability and Security through Alternative Authentication Methods

With the expanding popularity of various Internet services, online users have be- come more vulnerable to malicious attacks as more of their private information is accessible on the Internet. The primary defense protecting private information is user authentication, which currently relies on less than ideal methods such as text passwords and PIN numbers. Alternative methods such as graphical passwords and behavioral biometrics have been proposed, but with too many limitations to replace current methods. However, with enhancements to overcome these limitations and harden existing methods, alternative authentications may become viable for future use. This dissertation aims to enhance the viability of alternative authentication systems. In particular, our research focuses on graphical passwords, biometrics that depend, directly or indirectly, on anthropometric data, and user authentication en- hancements using touch screen features on mobile devices. In the study of graphical passwords, we develop a new cued-recall graphical pass- word system called GridMap by exploring (1) the use of grids with variable input entered through the keyboard, and (2) the use of maps as background images. as a result, GridMap is able to achieve high key space and resistance to shoulder surfing attacks. to validate the efficacy of GridMap in practice, we conduct a user study with 50 participants. Our experimental results show that GridMap works well in domains in which a user logs in on a regular basis, and provides a memorability benefit if the chosen map has a personal significance to the user. In the study of anthropometric based biometrics through the use of mouse dy- namics, we present a method for choosing metrics based on empirical evidence of natural difference in the genders. In particular, we develop a novel gender classifi- cation model and evaluate the model’s accuracy based on the data collected from a group of 94 users. Temporal, spatial, and accuracy metrics are recorded from kine- matic and spatial analyses of 256 mouse movements performed by each user. The effectiveness of our model is validated through the use of binary logistic regressions. Finally, we propose enhanced authentication schemes through redesigned input, along with the use of anthropometric biometrics on mobile devices. We design a novel scheme called Triple Touch PIN (TTP) that improves traditional PIN number based authentication with highly enlarged keyspace. We evaluate TTP on a group of 25 participants. Our evaluation results show that TTP is robust against dictio- nary attacks and achieves usability at acceptable levels for users. We also assess anthropometric based biometrics by attempting to differentiate user fingers through the readings of the sensors in the touch screen. We validate the viability of this biometric approach on 33 users, and observe that it is feasible for distinguishing the fingers with the largest anthropometric differences, the thumb and pinkie fingers

Predicting sex as a soft-biometrics from device interaction swipe gestures

Touch and multi-touch gestures are becoming the most common way to interact with technology such as smart phones, tablets and other mobile devices. The latest touch-screen input capacities have tremendously increased the quantity and quality of available gesture data, which has led to the exploration of its use in multiple disciplines from psychology to biometrics. Following research studies undertaken in similar modalities such as keystroke and mouse usage biometrics, the present work proposes the use of swipe gesture data for the prediction of soft-biometrics, specifically the user's sex. This paper details the software and protocol used for the data collection, the feature set extracted and subsequent machine learning analysis. Within this analysis, the BestFirst feature selection technique and classification algorithms (naïve Bayes, logistic regression, support vector machine and decision tree) have been tested. The results of this exploratory analysis have confirmed the possibility of sex prediction from the swipe gesture data, obtaining an encouraging 78% accuracy rate using swipe gesture data from two different directions. These results will hopefully encourage further research in this area, where the prediction of soft-biometrics traits from swipe gesture data can play an important role in enhancing the authentication processes based on touch-screen devices

The influence of age and gender in the interaction with touch screens

Touch screens are nowadays one of the major interfaces in the interaction between humans and technology, mostly due to the significant growth in the use of smartphones and tablets in the last years. This broad use, that reaches people from all strata of society, makes touch screens a relevant tool to study the mechanisms that influence the way we interact with electronic devices. In this paper we collect data regarding the interaction patterns of different users with mobile devices. We present a way to formalize these interaction patterns and analyze how aspects such as age and gender influence them. The results of this research may be relevant for developing mobile applications that identify and adapt to the users or their characteristics, including impairments in fine motor skills or in cognitive function.Fundos Europeus Estruturais e de Investimento (FEEI) through Programa Operacional Regional Norte, in the scope of project NORTE01-0145-FEDER-02357

Exploiting behavioral biometrics for user security enhancements

As online business has been very popular in the past decade, the tasks of providing user authentication and verification have become more important than before to protect user sensitive information from malicious hands. The most common approach to user authentication and verification is the use of password. However, the dilemma users facing in traditional passwords becomes more and more evident: users tend to choose easy-to-remember passwords, which are often weak passwords that are easy to crack. Meanwhile, behavioral biometrics have promising potentials in meeting both security and usability demands, since they authenticate users by who you are , instead of what you have . In this dissertation, we first develop two such user verification applications based on behavioral biometrics: the first one is via mouse movements, and the second via tapping behaviors on smartphones; then we focus on modeling user web browsing behaviors by Fitts\u27 Law.;Specifically, we develop a user verification system by exploiting the uniqueness of people\u27s mouse movements. The key feature of our system lies in using much more fine-grained (point-by-point) angle-based metrics of mouse movements for user verification. These new metrics are relatively unique from person to person and independent of the computing platform. We conduct a series of experiments to show that the proposed system can verify a user in an accurate and timely manner, and induced system overhead is minor. Similar to mouse movements, the tapping behaviors of smartphone users on touchscreen also vary from person to person. We propose a non-intrusive user verification mechanism to substantiate whether an authenticating user is the true owner of the smartphone or an impostor who happens to know the passcode. The effectiveness of the proposed approach is validated through real experiments. to further understand user pointing behaviors, we attempt to stress-test Fitts\u27 law in the wild , namely, under natural web browsing environments, instead of restricted laboratory settings in previous studies. Our analysis shows that, while the averaged pointing times follow Fitts\u27 law very well, there is considerable deviations from Fitts\u27 law. We observe that, in natural browsing, a fast movement has a different error model from the other two movements. Therefore, a complete profiling on user pointing performance should be done in more details, for example, constructing different error models for slow and fast movements. as future works, we plan to exploit multiple-finger tappings for smartphone user verification, and evaluate user privacy issues in Amazon wish list

Can Paradata Predict Interviewer Effects?

Consideration of interviewer effects (interviewer measurement error variance) in active quality control does not seem widespread despite its known effect on reducing precision of survey estimates. One major obstacle is that interviewer effect estimates computed on partial data (as a survey is in progress) can be very unstable. We address this issue by exploring the use of paradata (keystrokes and time stamps generated during the computer-assisted interviewing process) as proxies of interviewer effects with a focus on large-scale repeated cross-section or panel surveys. We first estimate interviewer effects for each item in our analysis by using multilevel models that include a vector of respondent covariates to approximate interpenetration. We then compute the proportion of variance explained when we add interviewer-level paradata inputs to this model. These inputs are selected using adaptive lasso from a pool of thirteen measures. Realistic predictions of the explained variance are then computed using a bootstrap-based method. We use data from the 2015 wave of the Panel Study of Income Dynamics (PSID) for our analysis and find promising results - paradata explain more than half the magnitude of interviewer effects on average across items. Also, paradata outperformed interviewer-level demographic and work-related variables in explaining interviewer effects. While most of the focus in the literature and practice has been on time-based paradata, e.g., item times, we find that non-time based paradata, e.g., frequency of item revisits, outperform the time-based paradata for a large majority of items. We conclude by discussing how survey organizations can use these findings in active quality control to contain interviewer effects