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We present a system for identifying humans by their walking sounds. Thisproblem is also known as acoustic gait recognition. The goal of the system isto analyse sounds emitted by walking persons (mostly the step sounds) andidentify those persons. These sounds are characterised by the gait pattern andare influenced by the movements of the arms and legs, but also depend on thetype of shoe. We extract cepstral features from the recorded audio signals anduse hidden Markov models for dynamic classification. A cyclic model topology isemployed to represent individual gait cycles. This topology allows to model anddetect individual steps, leading to very promising identification rates. Forexperimental validation, we use the publicly available TUM GAID database, whichis a large gait recognition database containing 3050 recordings of 305 subjectsin three variations. In the best setup, an identification rate of 65.5 % isachieved out of 155 subjects. This is a relative improvement of almost 30 %compared to our previous work, which used various audio features and supportvector machines.

Acoustic Gait-based Person Identification using Hidden Markov Models