Improved Gait Recognition Performance Using Cycle Matching

This paper presents a user authentication method based on gait (walking style). Human gait (in terms of acceleration signal) is collected using a wearable accelerometer sensor attached to the ankle of the person. Ankle accelerations from three directions (up-down, forward-backward and sideways) are utilized for identity verification purposes. Applying cycle matching method on experimental data from 30 subjects, we obtained an encouraging EER (Equal Error Rate) of 1.6% using sideway acceleration signals. In addition, our analysis indicate that performance is better with light shoes than with heavy shoes; and sideways motion appear to provide higher discrimination power compared to the up-down and forward-backward motions. Application area for such gait-based user authentication approach can be in improving security and usability of user authentication in emerging applications such as pervasive environment.