Biometric Keys for the Encryption of Multimodal Signatures

Electricity, electromagnetism & magnetis

Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

Active authentication refers to the process in which users are unobtrusively monitored and authenticated continuously throughout their interactions with mobile devices. Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users. Normally, the enrolled user is considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise and an autoencoder are used to model the pseudo-negative class and to regularize the network to learn meaningful feature representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. Effectiveness of the proposed framework is demonstrated using three publically available face-based active authentication datasets and it is shown that the proposed method achieves superior performance compared to the traditional one class classification methods. The source code is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201

Linking recorded data with emotive and adaptive computing in an eHealth environment

Telecare, and particularly lifestyle monitoring, currently relies on the ability to detect and respond to changes in individual behaviour using data derived from sensors around the home. This means that a significant aspect of behaviour, that of an individuals emotional state, is not accounted for in reaching a conclusion as to the form of response required. The linked concepts of emotive and adaptive computing offer an opportunity to include information about emotional state and the paper considers how current developments in this area have the potential to be integrated within telecare and other areas of eHealth. In doing so, it looks at the development of and current state of the art of both emotive and adaptive computing, including its conceptual background, and places them into an overall eHealth context for application and development

WoX+: A Meta-Model-Driven Approach to Mine User Habits and Provide Continuous Authentication in the Smart City

The literature is rich in techniques and methods to perform Continuous Authentication (CA) using biometric data, both physiological and behavioral. As a recent trend, less invasive methods such as the ones based on context-aware recognition allows the continuous identification of the user by retrieving device and app usage patterns. However, a still uncovered research topic is to extend the concepts of behavioral and context-aware biometric to take into account all the sensing data provided by the Internet of Things (IoT) and the smart city, in the shape of user habits. In this paper, we propose a meta-model-driven approach to mine user habits, by means of a combination of IoT data incoming from several sources such as smart mobility, smart metering, smart home, wearables and so on. Then, we use those habits to seamlessly authenticate users in real time all along the smart city when the same behavior occurs in different context and with different sensing technologies. Our model, which we called WoX+, allows the automatic extraction of user habits using a novel Artificial Intelligence (AI) technique focused on high-level concepts. The aim is to continuously authenticate the users using their habits as behavioral biometric, independently from the involved sensing hardware. To prove the effectiveness of WoX+ we organized a quantitative and qualitative evaluation in which 10 participants told us a spending habit they have involving the use of IoT. We chose the financial domain because it is ubiquitous, it is inherently multi-device, it is rich in time patterns, and most of all it requires a secure authentication. With the aim of extracting the requirement of such a system, we also asked the cohort how they expect WoX+ will use such habits to securely automatize payments and identify them in the smart city. We discovered that WoX+ satisfies most of the expected requirements, particularly in terms of unobtrusiveness of the solution, in contrast with the limitations observed in the existing studies. Finally, we used the responses given by the cohorts to generate synthetic data and train our novel AI block. Results show that the error in reconstructing the habits is acceptable: Mean Squared Error Percentage (MSEP) 0.04%

Activity related biometrics for person authentication

One of the major challenges in human-machine interaction has always been the development of such techniques that are able to provide accurate human recognition, so as to other either personalized services or to protect critical infrastructures from unauthorized access. To this direction, a series of well stated and efficient methods have been proposed mainly based on biometric characteristics of the user. Despite the significant progress that has been achieved recently, there are still many open issues in the area, concerning not only the performance of the systems but also the intrusiveness of the collecting methods. The current thesis deals with the investigation of novel, activity-related biometric traits and their potential for multiple and unobtrusive authentication based on the spatiotemporal analysis of human activities. In particular, it starts with an extensive bibliography review regarding the most important works in the area of biometrics, exhibiting and justifying in parallel the transition that is performed from the classic biometrics to the new concept of behavioural biometrics. Based on previous works related to the human physiology and human motion and motivated by the intuitive assumption that different body types and different characters would produce distinguishable, and thus, valuable for biometric verification, activity-related traits, a new type of biometrics, the so-called prehension biometrics (i.e. the combined movement of reaching, grasping activities), is introduced and thoroughly studied herein. The analysis is performed via the so-called Activity hyper-Surfaces that form a dynamic movement-related manifold for the extraction of a series of behavioural features. Thereafter, the focus is laid on the extraction of continuous soft biometric features and their efficient combination with state-of-the-art biometric approaches towards increased authentication performance and enhanced security in template storage via Soft biometric Keys. In this context, a novel and generic probabilistic framework is proposed that produces an enhanced matching probability based on the modelling of the systematic error induced during the estimation of the aforementioned soft biometrics and the efficient clustering of the soft biometric feature space. Next, an extensive experimental evaluation of the proposed methodologies follows that effectively illustrates the increased authentication potential of the prehension-related biometrics and the significant advances in the recognition performance by the probabilistic framework. In particular, the prehension biometrics related biometrics is applied on several databases of ~100 different subjects in total performing a great variety of movements. The carried out experiments simulate both episodic and multiple authentication scenarios, while contextual parameters, (i.e. the ergonomic-based quality factors of the human body) are also taken into account. Furthermore, the probabilistic framework for augmenting biometric recognition via soft biometrics is applied on top of two state-of-art biometric systems, i.e. a gait recognition (> 100 subjects)- and a 3D face recognition-based one (~55 subjects), exhibiting significant advances to their performance. The thesis is concluded with an in-depth discussion summarizing the major achievements of the current work, as well as some possible drawbacks and other open issues of the proposed approaches that could be addressed in future works.Open Acces