Gait-based person identification by spectral, cepstral and energy-related audio features.

With this work, we address the problem of acoustic gait-based person identification, which is the task of identifying humans by the sounds they make while walking. We examine several acoustic features from speech processing tasks for their suitability for acoustic gait recognition. Using a wrapper-based feature selection technique, we reduce the feature set while at the same time increasing the identification accuracy by 10% (relative). For classification, Support Vector Machines (SVMs) are employed. Experiments are conducted using the TUM GAID database, which is a large gait recognition database containing 3 050 recordings of 305 subjects in three variations.