Robust Gait Recognition by Integrating Inertial and RGBD Sensors

摘要: Gait has been considered as a promising and unique biometric for person identification. Traditionally, gait data are collected using either color sensors, such CCD camera, depth Microsoft Kinect, or inertial an accelerometer. However, single type of sensors may only capture part the dynamic features make recognition sensitive to complex covariate conditions, leading fragile gait-based identification systems. In this paper, we propose combine all three types collection recognition, which can be used important applications, identity access restricted building area. We two new algorithms, namely EigenGait TrajGait, extract from RGBD (color depth) data, respectively. Specifically, extracts general dynamics accelerometer readings in eigenspace TrajGait more detailed subdynamics by analyzing 3-D dense trajectories. Finally, both extracted fed into supervised classifier Experiments on 50 subjects, with comparisons several other state-of-the-art gait-recognition approaches, show that proposed approach achieve higher accuracy robustness.