Robust Gait Authentication Using Autoencoder and Decision Tree

Abstract

Various biometric authentication technologies have been developed for protecting smartphones against unauthorized access. Most authentication methods provide highly accurate authentication; however, an unlocked device can be used freely until it is re-locked. This study proposes a robust gait-based authentication method that identifies various walking styles using only a smartphone accelerometer. However, walking motion is dependent on individuals and their walking style. Based on features extracted from acceleration data, the proposed method first introduces a decision tree for classifying walking style prior to verifying identity. Then, identification is performed using the reconstruction error of the autoencoder for a specified walking style. Results confirm the effectiveness of the proposed method, which utilizes the novel approach of combining two simple methods to achieve superior performance.