Advanced Homological Analysis for Biometric Identification Using Accelerometer

Biometric information acquired by mobile devices has been explored for various emerging applications in recent years. One of these new applications is advanced authentication. On-line user-identification based on biometrics collected by smartphones has been drawing tremendous research interest lately. The accelerometer data are available pervasively on cellular phones and therefore there incurs no extra cost to facilitate accelerometer-based biometric systems. Individual time-series constituted by accelerometer data are quite complex and therefore impose challenges to the user-identification research. In this work, we propose a novel on-line user-identification approach using accelerometer data by exploiting new homological analysis. The inherent walking patterns of different users in the accelerometer data are monitored in the embedded phase space. Then the expected persistence diagrams (EPDs) are formed and transformed into the corresponding probability distribution functions. Thus, the discrepancy in walking patterns can be measured by the Kullback-Leibler divergence (KLD) of those ultimate distribution functions. Users can be identified according to the KLDs resulting from the sampled data and pre-stored training data. Experimental results from the real-world accelerometer data have demonstrated that the accuracy of our proposed new scheme reaches up to 94.5% and it greatly outperforms other existing methods.