Smartwatch-based Gait Authentication Using Siamese LSTM Networks

Smartwatches have become a key component in wearable computing. These devices are tightly woven with the modern smartphone and the user themselves. However, smartwatches lack the ability to employ well-known biometric authentication techniques such as fingerprint scanning and facial recognition due to their physical space constraints and other challenges. Gait analysis is the study of walking patterns, and it is known to be a feasible authentication technique, especially using the accelerometer and gyroscope sensors within smartphones. This study explores the feasibility of a gait-based authentication scheme that uses accelerometer and gyroscope data from a commercially available smartwatch device. A novel approach of using Lightweight Siamese long-short term memory (LSTM) networks to identify unique features from two sets of sensor data is proposed for performing gait-based authentication. Furthermore, the computational costs of executing this type of authentication scheme are also explored with the proposed LSTM model architecture.