Quantifying Gait Similarity: User Authentication and Real-World Challenge
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Robust palmprint verification using 2D and 3D features
Pattern Recognition
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
Multibiometric cryptosystem: model structure and performance analysis
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
Analysis of gait in patients with normal pressure hydrocephalus
Proceedings of the First ACM Workshop on Mobile Systems, Applications, and Services for Healthcare
Gait verification using knee acceleration signals
Expert Systems with Applications: An International Journal
SAPHE: simple accelerometer based wireless pairing with heuristic trees
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
| Hi-index | 0.00 |
Research in biometric gait recognition has increased. Earlier gait recognition works reported promising results, usually with a small sample size. Recent studies with a larger sample size confirm gait potential as a biometric from which individuals can be identified. Despite much research being carried out in gait recognition, the topic of vulnerability of gait to attacks has not received enough attention. In this paper, an analysis of minimal-effort impersonation attack and the closest person attack on gait biometrics are presented. Unlike most previous gait recognition approaches, where gait is captured using a (video) camera from a distance, in our approach, gait is collected by an accelerometer sensor attached to the hip of subjects. Hip acceleration in three orthogonal directions (up-down, forward-backward, and sideways) is utilized for recognition. We have collected 760 gait sequences from 100 subjects. The experiments consisted of two parts. In the first part, subjects walked in their normal walking style, and using the averaged cycle method, an EER of about 13% was obtained. In the second part, subjects were trying to walk as someone else. Analysis based on FAR errors indicates that a minimal-effort impersonation attack on gait biometric does not necessarily improve the chances of an impostor being accepted. However, attackers with knowledge of their closest person in the database can be a serious threat to the authentication system.