Gait template protection using HMM-UBM

Abstract

This paper presents a hidden Markov model-Universal background model gait authentication system, which is also incorporated into a template protection based on a fuzzy commitment scheme.We show that with limited enrollment data the HMM-UBM system achieves a very competitive equal error rate of 1% using one sensor. The proposed template protection scheme benefits from eigenfeatures coming from multiple Universal background model systems fused with a novel technique that minimizes the bit error rate for genuine attempts. This allows the protected system to achieve a false rejection rate below 5% with an effective key length of 64 bits.