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

Abstract

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. A. Sundararajan et al. • TBS consider each modality of the user as a separate entity while WBS consider the entire user (along with all of their individual modalities) as one [152] • TBS can be optionally connected to the Internet, while WBS are inherently online, exploiting the principles of Internet of Everything (IoE) [178] • The authentication and identification processes for TBS are static and user-initiated while for WBS are dynamic and autonomous

Soft biometrics: a survey

“…Usually, the intrusive features are fingerprints [37], retina scans [186] etc. They are referred as traditional or hard biometrics [188]. The non-intrusive features are called soft biometrics.…”

“…These are demographic, global, anthropometric, material, behavioural and medical etc. [45,188]. This set of soft biometrics features can be estimated or extracted from whole human body beside face or head only.…”

“…The field of soft biometrics is emerging as a potential alternate to traditional biometrics in recent years [29]. There are multiple reasons behind it, like non-intrusive nature of features or traits [45], independence at modality and feature level [188], presence of semantic description for each individual trait [49] and finally, a seamless method for recognition and retrieval [46]. However, the field is still facing several challenges and there are number of gaps that need to be filled before declaring it a replacement for traditional biometrics.…”

Adapting big data standards, maturity models to smart grid distributed generation: critical review

“…However, understandably so, there are no references to management, protection, or exchange of data within this context. The domains of communication, information exchange, and cybersecurity all contain standards for DG that provide some detail on how data are exchanged domain [61–65, 104–112, 131, 132]. Further, there are three DG or DG related standards that discuss how data will be protected from attack [113, 114, 122, 133–135].…”

Case study on the effects of partial solar eclipse on distributed PV systems and management areas

“…The study is divided into four analyses during the eclipse peak: performance measurement and relationship analysis for the systems, power quality analysis at the point of interconnection (POI) of system , simulation of voltage device operations on an IEEE 8500 test case distribution feeder network remodelled to include real system parameters of the feeders that the two PV systems connect to, and finally, system reliability evaluation and forecasting for the utility management areas (Miami and Daytona). Different datasets are used for conducting these analyses after being subject to quality checks [10, 11]: real‐time‐series PV generation data of 1 min resolution collected from cloud‐based on‐site data acquisition systems (DASs) for system performance; real‐time‐series average root‐mean‐square (RMS) voltage, harmonics (voltage, current), and instantaneous flicker (IFL), short‐term flicker , and long‐term flicker data of 1 min resolution collected from the POI of system using a metre for power quality analysis; smart inverter data sheets (for power‐efficiency curves), PV data sheets (for temperature‐efficiency curve), and load profile data for the voltage profile analysis; moreover, hourly weather data from the National Climatic Data Centre and daily reliability data for the two management areas for the reliability analysis.…”