A large scale footstep database for biometric studies created using cross-biometrics for labelling

This paper describes a semi-automatic system to capture and label a reasonable size biometric database. In our case, the biometric to be assessed are footstep signals, but the system could be extendable to other biometrics. Extra biometric data such as the voice and video recordings of the face and the gait are used to assist the database labelling to minimise the error. Thus, audio identifier recordings are used to automatically label the database with a speaker recognition system achieving results of 0.15% of equal error rate (EER) of person verification using Gaussian mixture models (GMM). Also, a footstep detector system has been developed to reduce the presence of invalid signals from the database having a percentage of less than 1% of correct footsteps miss-classified using features from the ground reaction force (GRF) and using a support vector machine (SVM) classifier. To date, more than 20,000 footstep signals have been collected from more than 100 people, which is well beyond previously reported databases. The database is collected in different sessions which will allow us to study how different factors such as footwear, the person carrying a load or different walking speeds affect the recognition of persons using their footsteps.