Supervised learning methods for biometric authentication on mobile devices
نویسندگان
چکیده
We develop fraud detection and user authentication classifiers for mobile keystroke and haptic patterns, achieving 84% accuracy, 90% recall, and 81% precision within one model architecture, and 99% recall and 83% precision across all models. In addition to proposing these models that outperform existing touch dynamics authentication models, we present a secure, space-efficient, and extensible framework for real-time biometric backlogging comparison.
منابع مشابه
Challenges and Research Directions for Adaptive Biometric Recognition Systems
Biometric authentication using mobile devices is becoming a convenient and important means to secure access to remote services such as telebanking and electronic transactions. Such an application poses a very challenging pattern recognition problem: the training samples are often sparse and they cannot represent the biometrics of a person. The query features are easily affected by the acquisiti...
متن کاملRecognition of Face Using Neural Network
Advancement in Artificial Intelligence has lead to the developments of various “smart” devices. The task of face Recognition has been actively researched in recent years. Wide usage of biometric information for person identity verification purposes, terrorist acts prevention measures and authentication process simplification in computer systems has raised significant attention to reliability an...
متن کاملTemplate Protection for Biometric Gait Data
Biometric gait recognition is a well suited method for authentication on mobile devices as it is unobtrusive and concurrent. Hence, in contrast to PIN authentication it is no extra-effort for the user. The characteristic gait of a subject can be recorded using accelerometers which are nowadays already contained in many mobile devices. From this data biometric feature vectors can be extracted an...
متن کاملNumeric-Passcode Keystroke Biometric Studies on Smartphones
A keystroke biometric classification system traditionally used on data captured from physical keyboards associated with laptops and personal computers, was extended to evaluate biometric data extracted from mobile devices. The results were compared to the results of similar studies utilizing the same data inputs. Additional results were extracted; including results tied to features native to mo...
متن کاملMulti-modal Behavioural Biometric Authentication for Mobile Devices
The potential advantages of behavioural biometrics are that they can be utilised in a transparent (non-intrusive) and continuous authentication system. However, individual biometric techniques are not suited to all users and scenarios. One way to increase the reliability of transparent and continuous authentication systems is create a multi-modal behavioural biometric authentication system. Thi...
متن کامل