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

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

Privacy-Protecting Techniques for Behavioral Data: A Survey

Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved

Novel neural approaches to data topology analysis and telemedicine

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen676. INGEGNERIA ELETTRICAnoopenRandazzo, Vincenz

Towards Private Biometric Authentication and Identification

Handwriting and speech are important parts of our everyday lives. Handwriting recognition is the task that allows the recognizing of written text, whether it be letters, words or equations, from given data. When analyzing handwriting, we can analyze static images or the recording of written text through sensors. Handwriting recognition algorithms can be used in many applications, including signature verification, electronic document processing, as well as e-security and e-health related tasks. The OnHW datasets consists of a set of datasets which, through the use of various sensors, captures the writing of characters, words, symbols and equations, recorded in the form of multivariate time series. We begin by developing character recognition models, targeting letters (and later symbols), trained and tested using the OnHW-chars dataset (and later the split OnHW-equations dataset). Our models were able to improve upon the accuracy of the previous best results on both datasets explored. Using our machine learning (ML) models, we provide 11.3%-23.56% improvements over the previous best ML models. Using deep learning (DL), as well as ensemble techniques, we were able to improve on the best previous models by 3.08%-7.01%. In addition to the accuracy improvements, we aim to provide some level of explainability, using a specialized version of LIME for time series data. This explanation helps provide some rationale for why the models make sense for the data, as well as why ensemble methods may be useful to improve accuracy rates for this task. To verify the robustness of our models trained over the OnHW-chars dataset, we trained our DL models using the same model parameters over a more recently published OnHW-equations dataset. Our DL models with ensemble learning provide 0.05%-4.75% improvements over the previous best DL models. While the character recognition task has many applications, when using it to provide a service, it is important to consider user privacy since handwriting is biometric data and contains private information. Next, we design a framework that uses multiparty computation (MPC) to provide users with privacy over their handwritten data, when providing a service for character recognition. We then implement the framework using the models trained on public data to provide private inference on hidden user data. This framework is implemented in the CrypTen MPC framework. We obtain results on the accuracy difference of the models when making inference using MPC, as well as the costs associated with performing this inference. We found a 0.55%-1.42% accuracy difference between plaintext inference and inference with MPC. Next, we pivot to explore writer identification, which involves identifying the writer of some handwritten text. We use the OnHW-equations dataset for our analysis, which at the time of writing has not been used for this task before. We first analyze and reformat the data to fit the writer identification task, as well as remove bias. Using DL models, we obtain accuracy results of up to 91.57% in identifying the writer using their handwriting. As with private inference in the character recognition task, it is important to account for user privacy when training writer identification models and making inference. We design and implement a framework for private training and inference for the writer recognition task, using the CrypTen MPC framework. Since training these models is very costly, we use simpler CNN's for private writer recognition. The chosen CNN trained privately in MPC obtained an accuracy of 77.45%. Next, we analyze the costs associated with privately training the CNN and other CNN's with altered model architectures. Finally, we switch to explore voice as a biometric in the speaker verification task. As with handwriting, a person's voice contains unique characteristics which can be used to determine the speaker. Not only can voice be analyzed similarly with handwriting, in that we can explore the speech recognition and speaker identification tasks, it comes with similar privacy risks for users. We design and implement a unique framework for private speaker verification using the MP-SPDZ MPC framework. We analyze the costs associated with training the model and making inferences, with our main goal being to determine the time it takes to make private inference. We then used these times as part of a survey conducted to determine how much people value the privacy of their biometrics and how long they were willing to wait for the increased privacy. We found that people were willing to tolerate significant time delays in order to privately authenticate themselves, when primed with the benefits of using MPC for privacy