23,819 research outputs found
Multimodal person recognition for human-vehicle interaction
Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies
CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping
With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving 25 subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over 99% average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as 4%
Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication
We investigate whether a classifier can continuously authenticate users based
on the way they interact with the touchscreen of a smart phone. We propose a
set of 30 behavioral touch features that can be extracted from raw touchscreen
logs and demonstrate that different users populate distinct subspaces of this
feature space. In a systematic experiment designed to test how this behavioral
pattern exhibits consistency over time, we collected touch data from users
interacting with a smart phone using basic navigation maneuvers, i.e., up-down
and left-right scrolling. We propose a classification framework that learns the
touch behavior of a user during an enrollment phase and is able to accept or
reject the current user by monitoring interaction with the touch screen. The
classifier achieves a median equal error rate of 0% for intra-session
authentication, 2%-3% for inter-session authentication and below 4% when the
authentication test was carried out one week after the enrollment phase. While
our experimental findings disqualify this method as a standalone authentication
mechanism for long-term authentication, it could be implemented as a means to
extend screen-lock time or as a part of a multi-modal biometric authentication
system.Comment: to appear at IEEE Transactions on Information Forensics & Security;
Download data from http://www.mariofrank.net/touchalytics
- …