Personal identification method using footsteps

In recent years, personal authentications using biological information are used for protection of personal data and confidential information in local governments and companies. In this paper, we propose a novel personal identification method using footsteps. The users´ mental burdens of the proposal technique is a little because the footsteps can be easily recorded without special equipments. First, the proposed method detects footstep sections from the recorded signals. Then, the acoustic feature parameters, which are Mel-Frequency Cepstral Coefficients (MFCCs), ΔMFCCs, and ΔLogarithm Powers (ΔLPs), are extracted as footstep features from the footstep section. Finally, persons are identified by k-Nearest Neighbor (k-NN) in which Dynamic Programming matching algorithm (DP) is used as a distance measure and/or Gaussian Mixture Models (GMMs). We conduct personal identification experiments using 720 footstep data which are recorded from 12 test subjects for evaluating the proposed method. From the experimental results, average accuracies of overall footwear are 79.9% and 92.8% in k-NN and GMMs.