A Survey on Modality Characteristics, Performance Evaluation Metrics, and Security for Traditional and Wearable Biometric Systems

Biometric research is directed increasingly towards Wearable Biometric Systems (WBS) for user authentication and identification. However, prior to engaging in WBS research, how their operational dynamics and design considerations differ from those of Traditional Biometric Systems (TBS) must be understood. While the current literature is cognizant of those differences, there is no effective work that summarizes the factors where TBS and WBS differ, namely, their modality characteristics, performance, security and privacy. To bridge the gap, this paper accordingly reviews and compares the key characteristics of modalities, contrasts the metrics used to evaluate system performance, and highlights the divergence in critical vulnerabilities, attacks and defenses for TBS and WBS. It further discusses how these factors affect the design considerations for WBS, the open challenges and future directions of research in these areas. In doing so, the paper provides a big-picture overview of the important avenues of challenges and potential solutions that researchers entering the field should be aware of. Hence, this survey aims to be a starting point for researchers in comprehending the fundamental differences between TBS and WBS before understanding the core challenges associated with WBS and its design

Gait-based identification for elderly users in wearable healthcare systems

Abstract The increasing scope of sensitive personal information that is collected and stored in wearable healthcare devices includes physical, physiological, and daily activities, which makes the security of these devices very essential. Gait-based identity recognition is an emerging technology, which is increasingly used for the access control of wearable devices, due to its outstanding performance. However, gait-based identity recognition of elderly users is more challenging than that of young adults, due to significant intra-subject gait fluctuation, which becomes more pronounced with user age. This study introduces a gait-based identity recognition method used for the access control of elderly people-centred wearable healthcare devices, which alleviates the intra-subject gait fluctuation problem and provides a significant recognition rate improvement, as compared to available methods. Firstly, a gait template synthesis method is proposed to reduce the intra-subject gait fluctuation of elderly users. Then, an arbitration-based score level fusion method is defined to improve the recognition accuracy. Finally, the proposed method feasibility is verified using a public dataset containing acceleration signals from three IMUs worn by 64 elderly users with the age range from 50 to 79 years. The experimental results obtained prove that the average recognition rate of the proposed method reaches 96.7%. This makes the proposed method quite lucrative for the robust gait-based identification of elderly users of wearable healthcare devices

Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

Bioelectrical User Authentication

There has been tremendous growth of mobile devices, which includes mobile phones, tablets etc. in recent years. The use of mobile phone is more prevalent due to their increasing functionality and capacity. Most of the mobile phones available now are smart phones and better processing capability hence their deployment for processing large volume of information. The information contained in these smart phones need to be protected against unauthorised persons from getting hold of personal data. To verify a legitimate user before accessing the phone information, the user authentication mechanism should be robust enough to meet present security challenge. The present approach for user authentication is cumbersome and fails to consider the human factor. The point of entry mechanism is intrusive which forces users to authenticate always irrespectively of the time interval. The use of biometric is identified as a more reliable method for implementing a transparent and non-intrusive user authentication. Transparent authentication using biometrics provides the opportunity for more convenient and secure authentication over secret-knowledge or token-based approaches. The ability to apply biometrics in a transparent manner improves the authentication security by providing a reliable way for smart phone user authentication. As such, research is required to investigate new modalities that would easily operate within the constraints of a continuous and transparent authentication system. This thesis explores the use of bioelectrical signals and contextual information for non-intrusive approach for authenticating a user of a mobile device. From fusion of bioelectrical signals and context awareness information, three algorithms where created to discriminate subjects with overall Equal Error Rate (EER of 3.4%, 2.04% and 0.27% respectively. Based vii | P a g e on the analysis from the multi-algorithm implementation, a novel architecture is proposed using a multi-algorithm biometric authentication system for authentication a user of a smart phone. The framework is designed to be continuous, transparent with the application of advanced intelligence to further improve the authentication result. With the proposed framework, it removes the inconvenience of password/passphrase etc. memorability, carrying of token or capturing a biometric sample in an intrusive manner. The framework is evaluated through simulation with the application of a voting scheme. The simulation of the voting scheme using majority voting improved to the performance of the combine algorithm (security level 2) to FRR of 22% and FAR of 0%, the Active algorithm (security level 2) to FRR of 14.33% and FAR of 0% while the Non-active algorithm (security level 3) to FRR of 10.33% and FAR of 0%