Relative distance features for gait recognition with Kinect
This work addresses passive human recognition for biometric applications, but it is incremental as it builds on existing gait and anthropometric methods.
The study tackled gait recognition using Kinect by introducing relative distance-based gait features, achieving 85% accuracy comparable to anthropometric features, and over 95% when combined.
Gait and static body measurement are important biometric technologies for passive human recognition. Many previous works argue that recognition performance based completely on the gait feature is limited. The reason for this limited performance remains unclear. This study focuses on human recognition with gait feature obtained by Kinect and shows that gait feature can effectively distinguish from different human beings through a novel representation -- relative distance-based gait features. Experimental results show that the recognition accuracy with relative distance features reaches up to 85%, which is comparable with that of anthropometric features. The combination of relative distance features and anthropometric features can provide an accuracy of more than 95%. Results indicate that the relative distance feature is quite effective and worthy of further study in more general scenarios (e.g., without Kinect).