Curve aligning approach for gait authentication based on a wearable accelerometer

Gait authentication based on a wearable accelerometer is a novel biometric which can be used for identity identification, medical rehabilitation and early detection of neurological disorders. The method for matching gait patterns tells heavily on authentication performances. In this paper, curve aligning is introduced as a new method for matching gait patterns and it is compared with correlation and dynamic time warping (DTW). A support vector machine (SVM) is proposed to fuse pattern-matching methods in a decision level. Accelerations collected from ankles of 22 walking subjects are processed for authentications in our experiments. The fusion of curve aligning with backward-forward accelerations and DTW with vertical accelerations promotes authentication performances substantially and consistently. This fusion algorithm is tested repeatedly. Its mean and standard deviation of equal error rates are 0.794% and 0.696%, respectively, whereas among all presented non-fusion algorithms, the best one shows an EER of 3.03%.