Research on fastDTW and isolation forest-based lightweight gesture authentication

Abstract

This study explores the use of the accelerometer and gyroscope sensors in an Android mobile phone to capture data during user gesture performance. The recorded data is then processed and analyzed to extract feature values. The authentication of the user's identity may be achieved by this method, which is characterized by minimal constraints. This study initially compares the user's gesture to the template gesture using FastDTW. After that, an adaptive weighted vote determines the closest gesture template. This checks if the user is doing the template category correctly. The second stage extracts effective feature values from authentic user gestures. These feature values include efficient time-frequency domain feature values and the data extreme point spacing-to-length ratio. After that, authentic gestures are used to build the Isolation Forest to verify the user. This approach suggests a reduction in FRR, FAR, and an increase in accuracy, while using a smaller amount of data.