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

Designing Socially Acceptable Hand-to-Face Input

Wearable head-mounted displays combine rich graphical out- put with an impoverished input space. Hand-to-face gestures have been proposed as a way to add input expressivity while keeping control movements unobtrusive. To better understand how to design such techniques, we describe an elicitation study conducted in a busy public space in which pairs of users were asked to generate unobtrusive, socially acceptable hand- to-face input actions. Based on the results, we describe five design strategies: miniaturizing, obfuscating, screening, camouflaging and re-purposing. We instantiate these strategies in two hand-to-face input prototypes, one based on touches to the ear and the other based on touches of the thumbnail to the chin or cheek. Performance assessments characterize time and error rates with these devices. The paper closes with a validation study in which pairs of users experience the prototypes in a public setting and we gather data on the social acceptability of the designs and reflect on the effectiveness of the different strategies

SmoothMoves: Smooth Pursuits Head Movements for Augmented Reality

SmoothMoves is an interaction technique for augmented reality (AR) based on smooth pursuits head movements. It works by computing correlations between the movements of on-screen targets and the user???s head while tracking those targets. The paper presents three studies. The first suggests that head based input can act as an easier and more affordable surrogate for eye-based input in many smooth pursuits interface designs. A follow-up study grounds the technique in the domain of augmented reality, and captures the error rates and acquisition times on different types of AR devices: head-mounted (2.6%, 1965ms) and hand-held (4.9%, 2089ms). Finally, the paper presents an interactive lighting system prototype that demonstrates the benefits of using smooth pursuits head movements in interaction with AR interfaces. A final qualitative study reports on positive feedback regarding the technique???s suitability for this scenario. Together, these results indicate show SmoothMoves is viable, efficient and immediately available for a wide range of wearable devices that feature embedded motion sensing