Computational geometry: an introduction
Computational geometry: an introduction
Machine Learning
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Chaos and Time-Series Analysis
Chaos and Time-Series Analysis
Increasing Security of Mobile Devices by Decreasing User Effort in Verification
ICSNC '07 Proceedings of the Second International Conference on Systems and Networks Communications
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Error-correcting output codes: a general method for improving multiclass inductive learning programs
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Robustness of biometric gait authentication against impersonation attack
OTM'06 Proceedings of the 2006 international conference on On the Move to Meaningful Internet Systems: AWeSOMe, CAMS, COMINF, IS, KSinBIT, MIOS-CIAO, MONET - Volume Part I
Socially-Competent Computing Implementing Social Sensor Design
International Journal of Web-Based Learning and Teaching Technologies
Hi-index | 0.00 |
In this article, a novel technique for user's authentication and verification using gait as a biometric unobtrusive pattern is proposed. The method is based on a two stages pipeline. First, a general activity recognition classifier is personalized for an specific user using a small sample of her/his walking pattern. As a result, the system is much more selective with respect to the new walking pattern. A second stage verifies whether the user is an authorized one or not. This stage is defined as a one-class classification problem. In order to solve this problem, a four-layer architecture is built around the geometric concept of convex hull. This architecture allows to improve robustness to outliers, modeling non-convex shapes, and to take into account temporal coherence information. Two different scenarios are proposed as validation with two different wearable systems. First, a custom high-performance wearable system is built and used in a free environment. A second dataset is acquired from an Android-based commercial device in a `wild' scenario with rough terrains, adversarial conditions, crowded places and obstacles. Results on both systems and datasets are very promising, reducing the verification error rates by an order of magnitude with respect to the state-of-the-art technologies.