Gait Recognition using skeleton data

Biometric systems are becoming important since they provide efficient and more reliable means of human identity verification. Gait Recognition has created much interest in computer vision society over the last few years. In this paper, we have presented a Gait based human identification system using skeleton data acquired by using Microsoft Kinect sensor. The sensor acts as a digital eye which takes the color information as well as depth information through IR sensor. The static and dynamic features of each individual are extracted using the skeleton information. Classification is performed using two different algorithms. First is the Levenberg-Marquardt back propagation algorithm, second is the correlation algorithm. 90% recognition rate is achieved with correlation algorithm where as for Levenberg-Marquardt back propagation algorithm proposed system is able to achieve a recognition rate of 94% for 5 persons with fixed Kinect sensor setup.