On the Feasibility of Low-Cost Wearable Sensors for Multi-Modal Biometric Verification

Biometric systems designed on wearable technology have substantial differences from traditional biometric systems. Due to their wearable nature, they generally capture noisier signals and can only be trained with signals belonging to the device user (biometric verification). In this article, we assess the feasibility of using low-cost wearable sensors—photoplethysmogram (PPG), electrocardiogram (ECG), accelerometer (ACC), and galvanic skin response (GSR)—for biometric verification. We present a prototype, built with low-cost wearable sensors, that was used to capture data from 25 subjects while seated (at resting state), walking, and seated (after a gentle stroll). We used this data to evaluate how the different combinations of signals affected the biometric verification process. Our results showed that the low-cost sensors currently being embedded in many fitness bands and smart-watches can be combined to enable biometric verification. We report and compare the results obtained by all tested configurations. Our best configuration, which uses ECG, PPG and GSR, obtained 0.99 area under the curve and 0.02 equal error rate with only 60 s of training data. We have made our dataset public so that our work can be compared with proposals developed by other researchers.This work was supported by the CAM grant S2013/ICE-3095 (CIBERDINE: Cybersecurity, Data, and Risks) and by the MINECO grant TIN2016-79095-C2-2-R (SMOG-DEV—Security mechanisms for fog computing: advanced security for devices)

A Survey of Wearable Biometric Recognition Systems

The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people. Wearables equipped with an array of sensors are able to capture the owner’s physiological and behavioural traits, thus are well suited for biometric authentication to control other devices or access digital services. However, wearable biometrics have substantial differences from traditional biometrics for computer systems, such as fingerprints, eye features, or voice. In this article, we discuss these differences and analyse how researchers are approaching the wearable biometrics field. We review and provide a categorization of wearable sensors useful for capturing biometric signals. We analyse the computational cost of the different signal processing techniques, an important practical factor in constrained devices such as wearables. Finally, we review and classify the most recent proposals in the field of wearable biometrics in terms of the structure of the biometric system proposed, their experimental setup, and their results. We also present a critique of experimental issues such as evaluation and feasibility aspects, and offer some final thoughts on research directions that need attention in future work

A survey of wearable biometric recognition systems

The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people. Wearables equipped with an array of sensors are able to capture the owner's physiological and behavioural traits, thus are well suited for biometric authentication to control other devices or access digital services. However, wearable biometrics have substantial differences from traditional biometrics for computer systems, such as fingerprints, eye features, or voice. In this article, we discuss these differences and analyse how researchers are approaching the wearable biometrics field. We review and provide a categorization of wearable sensors useful for capturing biometric signals. We analyse the computational cost of the different signal processing techniques, an important practical factor in constrained devices such as wearables. Finally, we review and classify the most recent proposals in the field of wearable biometrics in terms of the structure of the biometric system proposed, their experimental setup, and their results. We also present a critique of experimental issues such as evaluation and feasibility aspects, and offer some final thoughts on research directions that need attention in future work.This work was partially supported by the MINECO grant TIN2013-46469-R (SPINY) and the CAM Grant S2013/ICE-3095 (CIBERDINE

Identifying Users with Wearable Sensors based on Activity Patterns

We live in a world where ubiquitous systems surround us in the form of automated homes, smart appliances and wearable devices. These ubiquitous systems not only enhance productivity but can also provide assistance given a variety of different scenarios. However, these systems are vulnerable to the risk of unauthorized access, hence the ability to authenticate the end-user seamlessly and securely is important. This paper presents an approach for user identification given the physical activity patterns captured using on-body wearable sensors, such as accelerometer, gyroscope, and magnetometer. Three machine learning classifiers have been used to discover the activity patterns of users given the data captured from wearable sensors. The recognition results prove that the proposed scheme can effectively recognize a user’s identity based on his/her daily living physical activity patterns

Authenticated Digital Avatars on Metaverse by Cyber Security Procedures

Metaverse is the  next generation Internet, aims to build a fully immersive, hyper spatiotemporal and self sustaining virtual shared space for humans to play, work, shop and socialize. In metaverse, users are  represented as digital avatars and using identity, user can shuttle across various virtual worlds (i.e., sub-metaverses) to experience a digital life, as well as make digital creations and economic interactions supported by physical infrastructures and the metaverse engine. Virtual reality headsets are the main devices used to access the Metaverse. Privacy and security concerns of the metaverse. The users need to verify their identity to log into the metaverse platforms, and the security of this phase becomes vital. In this paper, the user authentication methods such as Information-based authentication, biometric based authentication, and multi-model methods are reviewed and compared in terms of users security but in some cases these methods are failed to secure from cyber attacks. In this paper, we proposed,Token-based authentication method to enhance the security for the users to access and work on  the virtual environment

Non-invasive multi-modal human identification system combining ECG, GSR, and airflow biosignals

A huge amount of data can be collected through a wide variety of sensor technologies. Data mining techniques are often useful for the analysis of gathered data. This paper studies the use of three wearable sensors that monitor the electrocardiogram, airflow, and galvanic skin response of a subject with the purpose of designing an efficient multi-modal human identification system. The proposed system, based on the rotation forest ensemble algorithm, offers a high accuracy (99.6 % true acceptance rate and just 0.1 % false positive rate). For its evaluation, the proposed system was testing against the characteristics commonly demanded in a biometric system, including universality, uniqueness, permanence, and acceptance. Finally, a proof-of-concept implementation of the system is demonstrated on a smartphone and its performance is evaluated in terms of processing speed and power consumption. The identification of a sample is extremely efficient, taking around 200 ms and consuming just a few millijoules. It is thus feasible to use the proposed system on a regular smartphone for user identification.This work was supported by MINECO grant TIN2013- 46469-R (SPINY: Security and Privacy in the Internet of You) and CAM grant S2013/ICE-3095 (CIBERDINE: Cybersecurity, Data, and Risks)

A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors

Multimodal biometrics are promising for providing a strong security level for personal authentication, yet the implementation of a multimodal biometric system for practical usage need to meet such criteria that multimodal biometric signals should be easy to acquire but not easily compromised. We developed a wearable wrist band integrated with multispectral skin photomatrix (MSP) and electrocardiogram (ECG) sensors to improve the issues of collectability, performance and circumvention of multimodal biometric authentication. The band was designed to ensure collectability by sensing both MSP and ECG easily and to achieve high authentication performance with low computation, efficient memory usage, and relatively fast response. Acquisition of MSP and ECG using contact-based sensors could also prevent remote access to personal data. Personal authentication with multimodal biometrics using the integrated wearable wrist band was evaluated in 150 subjects and resulted in 0.2% equal error rate ( EER ) and 100% detection probability at 1% FAR (false acceptance rate) ( PD.1 ), which is comparable to other state-of-the-art multimodal biometrics. An additional investigation with a separate MSP sensor, which enhanced contact with the skin, along with ECG reached 0.1% EER and 100% PD.1 , showing a great potential of our in-house wearable band for practical applications. The results of this study demonstrate that our newly developed wearable wrist band may provide a reliable and easy-to-use multimodal biometric solution for personal authentication

Continuous and transparent multimodal authentication: reviewing the state of the art

Individuals, businesses and governments undertake an ever-growing range of activities online and via various Internet-enabled digital devices. Unfortunately, these activities, services, information and devices are the targets of cybercrimes. Verifying the user legitimacy to use/access a digital device or service has become of the utmost importance. Authentication is the frontline countermeasure of ensuring only the authorized user is granted access; however, it has historically suffered from a range of issues related to the security and usability of the approaches. They are also still mostly functioning at the point of entry and those performing sort of re-authentication executing it in an intrusive manner. Thus, it is apparent that a more innovative, convenient and secure user authentication solution is vital. This paper reviews the authentication methods along with the current use of authentication technologies, aiming at developing a current state-of-the-art and identifying the open problems to be tackled and available solutions to be adopted. It also investigates whether these authentication technologies have the capability to fill the gap between high security and user satisfaction. This is followed by a literature review of the existing research on continuous and transparent multimodal authentication. It concludes that providing users with adequate protection and convenience requires innovative robust authentication mechanisms to be utilized in a universal level. Ultimately, a potential federated biometric authentication solution is presented; however it needs to be developed and extensively evaluated, thus operating in a transparent, continuous and user-friendly manner

Activity-Based User Authentication Using Smartwatches

Smartwatches, which contain an accelerometer and gyroscope, have recently been used to implement gait and gesture- based biometrics; however, the prior studies have long-established drawbacks. For example, data for both training and evaluation was captured from single sessions (which is not realistic and can lead to overly optimistic performance results), and in cases when the multi-day scenario was considered, the evaluation was often either done improperly or the results are very poor (i.e., greater than 20% of EER). Moreover, limited activities were considered (i.e., gait or gestures), and data captured within a controlled environment which tends to be far less realistic for real world applications. Therefore, this study remedies these past problems by training and evaluating the smartwatch-based biometric system on data from different days, using large dataset that involved the participation of 60 users, and considering different activities (i.e., normal walking (NW), fast walking (FW), typing on a PC keyboard (TypePC), playing mobile game (GameM), and texting on mobile (TypeM)). Unlike the prior art that focussed on simply laboratory controlled data, a more realistic dataset, which was captured within un-constrained environment, is used to evaluate the performance of the proposed system. Two principal experiments were carried out focusing upon constrained and un-constrained environments. The first experiment included a comprehensive analysis of the aforementioned activities and tested under two different scenarios (i.e., same and cross day). By using all the extracted features (i.e., 88 features) and the same day evaluation, EERs of the acceleration readings were 0.15%, 0.31%, 1.43%, 1.52%, and 1.33% for the NW, FW, TypeM, TypePC, and GameM respectively. The EERs were increased to 0.93%, 3.90%, 5.69%, 6.02%, and 5.61% when the cross-day data was utilized. For comparison, a more selective set of features was used and significantly maximize the system performance under the cross day scenario, at best EERs of 0.29%, 1.31%, 2.66%, 3.83%, and 2.3% for the aforementioned activities respectively. A realistic methodology was used in the second experiment by using data collected within unconstrained environment. A light activity detection approach was developed to divide the raw signals into gait (i.e., NW and FW) and stationary activities. Competitive results were reported with EERs of 0.60%, 0% and 3.37% for the NW, FW, and stationary activities respectively. The findings suggest that the nature of the signals captured are sufficiently discriminative to be useful in performing transparent and continuous user authentication.University of Kuf