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A novel algorithm to recognise human identities via gait by body-worn accelerometers is presented. It uses acceleration information to measure human gait dynamics. Acceleration-based gait recognition is a non-intrusive biometric measurement, which is insensitive to changes of lighting conditions and viewpoint. The proposed algorithm first extracts signature points from gait acceleration signals, and then identifies the gait pattern using a signature point-based voting scheme. Experiments with a data set of 30 subjects show that the proposed algorithm significantly outperforms other existing methods and achieves a high recognition rate of 96.7% for the case of five accelerometers.