Biometric walk recognizer

Abstract

In this paper we present a comparative test of different approaches to gait recognition by smartphone accelerometer. Our work provides a twofold contribution. The first one is related to the use of low-cost, built-in sensors that nowadays equip most mobile devices. The second one is related to the use of our system in identification mode. Instead of being used to just verify the identity of the device owner, it can also be used for identification among a set of enrolled subjects. Whether the identification is carried out remotely or even if its results are transmitted to a server, the system can also be exploited in a multibiometric setting. Its results can be fused with those from computer-vision based gait recognition, as well as other biometric modalities, to enforce identification for accessing critical locations/services. We obtained the best results by matching complete walk captures (Recognition Rate 0.95), but the implicit limitation is represented by the fixed number of steps in the walks. Therefore we also investigated methods based on first dividing the signal into steps. The best of these achieved a Recognition Rate of 0.88.