High Performance Discovery In Time Series: Techniques And Case Studies (Monographs in Computer Science)
Gait analyzer based on a cell phone with a single three-axis accelerometer
Proceedings of the 8th conference on Human-computer interaction with mobile devices and services
Biometric daemons: authentication via electronic pets
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Toward accurate dynamic time warping in linear time and space
Intelligent Data Analysis
Unobtrusive multimodal biometric authentication: the HUMABIO project concept
EURASIP Journal on Advances in Signal Processing
uWave: Accelerometer-based personalized gesture recognition and its applications
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Gait recognition using wearable motion recording sensors
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Inference attacks on location tracks
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Touch me once and i know it's you!: implicit authentication based on touch screen patterns
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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In this paper an adaptive solution to secure the authentication process of cellular phones has been proposed. Gait and location tracks of the owner are used as the metrics for authentication. The cellular phone is envisioned to become as adaptive as a pet animal of the owner. The cellular phone learns various intrinsic attributes of the owner like his voice, face, hand and fingerprint geometry and interesting patterns in the owner's daily life and remembers those to continually check against any anomalous behavior that may occur due to the stealing of the phone. The checking is done level wise. Higher level of authentication is more stringent. Only when the cellular phone recognizes significant anomaly in a lower level, it goes one level up in the security hierarchy. The iPhone's accelerometer and A-GPS module have been utilized to record gait and location signatures. A fast and memory efficient variation of Dynamic Time Warping (DTW) algorithm called FastDTW has been used to compute the similarity score between gait samples.