Real Time Gait Recognition System Based on Kinect Skeleton Feature

Gait recognition is a kind of biometric feature recognition technique, which utilizes the pose of walking to recognize the identity. Generally people analyze the normal video data to extract the gait feature. These days, some researchers take advantage of Kinect to get the depth information or the position of joints for recognition. This paper mainly focus on the length of bones namely static feature and the angles of joints namely dynamic feature based on Kinect skeleton information. After preprocessing, we stored the two kinds of feature templates into database which we established for the system. For the static feature, we calculate the distance with Euclidean distance, and we calculated the distance in dynamic time warping algorithm (DTW) for the dynamic distance. We make a feature fusion for the distance between the static and dynamic. At last, we used the nearest neighbor (NN) classifier to finish the classification, and we got a real time recognition system and a good recognition result.