A Postural Information-Based Biometric Authentication System Employing S-Transform, Radial Basis Function Network, and Extended Kalman Filtering

This paper proposes a new system for biometry-based human authentication, where postural signal information is utilized to identify a person. The system employs a novel approach where four types of temporal postural signals are acquired for each person to develop an authentication database, and for each posture, both signals in the x- and y-directions are utilized for the purpose of authentication. The proposed system utilizes S-transform, which is a joint time-frequency representation tool, to determine the characteristic features for each human posture. Based on these characteristic features, a radial basis function network (RBFN) system is developed for the purpose of specific authentication. The RBFN authentication system is developed by training it to employ extended Kalman filtering (EKF). The EKF-trained RBFN authentication system could produce overall authentication accuracy on the order of 94%-95% and could outperform similar authentication systems developed, which employ two very popular variants of backpropagation neural networks (BPNNs) and a variant of radial basis neural network (RBNN).