Gait Verification using Deep Learning with a Pairwise Loss

A unique walking pattern to every individual makes gait a promising biometric. Gait is becoming an increasingly important biometric because it can be captured non-intrusively through accelerometers positioned at various locations on the human body. The advent of wearable sensors technology helps in collecting the gait data seamlessly at a low cost. Thus gait biometrics using accelerometers play a significant role in security-related applications like identity verification and recognition. In this work, we deal with the problem of identity verification using gait. As the data received through the sensors is indexed in time order, we consider identity verification through gait data as the time series binary classification problem. We present deep learning model with a pairwise loss function for the classification. We conducted experiments using two datasets: publicly available ZJU dataset of more than 150 subjects and our self-collected dataset with 15 subjects. With our model, we obtained an Equal Error Rate of 0.05% over ZJU dataset and 0.5% over our dataset, which shows that our model is superior to the state-of-the-art baselines.