Gait Recognition is a biometric application that aims to identify a person by analyzing his/her gait. Common methods for gait recognition rely on supervised machine learning techniques and step detection methods. However, the latter has been showed to provide poor performances in ambulatory conditions [4]. In this paper, a Granular Computing approach that does not require to detect steps is applied to the accelerometers signals obtained from gait. This approach involves information granules, based on density measures and collected from a reconstructed attractor, that can be obtained at different granularity levels and hence gather information at different scales. The performance of the method is evaluated on the task of recognizing 12 people by his/her gait.