Person verification based on skeleton biometrics by RGB-D camera

The state-of-the-art person identification and verification technologies require apparent human biological features as inputs explicitly. In many special scenarios, however, it is impossible to obtain the apparent human biological features since such features usually require intrusive human interaction directly. In this paper, we have proposed a hierarchical skeleton feature model (HSFM) for person verification by using implicit biometric skeletal data estimated from the emerging RGB-D camera. To validate the viability of the proposed feature model, experiments have been carried out to retrieve the estimated skeletal data from 11 volunteers by using the RGB-D camera. 2-fold cross-validations with SVM (Support Vector Machine) and KNN (K-Nearest Neighbors) methods were adopted and carried out 200 times to train and test the obtained data for regarding each participant as the authorized person. Furthermore, the parameter accuracy, sensitivity, specificity and time cost of the proposed human verification method were analyzed in the experiments. The intensive analyses on our data set have shown the feasibility to use the estimated human skeletal data from RGB-D camera for human verification.