I-vector analysis for Gait-based Person Identification using smartphone inertial signals

The i-vector approach is compared to a Gaussian Mixture Model-Universal Background Model (GMM-UBM) system, providing significant performance improvements when incorporating the PLDA compensation strategy: the best result reports a User Recognition Error Rate (URER) of 17.7%, an Equal Error Rate (EER) of 6.1% and an F1-score of 82.7% with 30 enrolled users. For less than six enrolled users, the URER error decreases to 5%.