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Person Identification Based on Static Features Extracted from Kinect Skeleton Data

In this paper, we present a study on person identification using static features extracted from Kinect skeleton data. On the contrary to previous reports that the dynamic features such as gait parameters are more discriminative than static features, we find that by using a combination of a set of easy to obtain static features, we can achieve nearly perfect accuracy in identifying persons with only a few frames. In our study, we experimented with several classifiers, including k-nearest neighbor (KNN), decision tree, Gaussian Naive Bayesian, neural network with multiplayer perception (MLP), and support vector machine (SVM), and several combinations of static skeleton features. In all scenarios, KNN outperforms other classifiers consistently. MLP and SVM require a huge amount of parameter tuning and training time and they do not perform well compared with KNN except for small gallery sizes when all static features available.