  Music Performers have their own idiosyncratic way of interpreting a musical
piece. A group of skilled performers playing the same piece of music would
likely to inject their unique artistic styles in their performances. The
variations of the tempo, timing, dynamics, articulation etc. from the actual
notated music are what make the performers unique in their performances. This
study presents a dataset consisting of four movements of Schubert's ``Sonata in
B-flat major, D.960" performed by nine virtuoso pianists individually. We
proposed and extracted a set of expressive features that are able to capture
the characteristics of an individual performer's style. We then present a
performer identification method based on the similarity of feature
distribution, given a set of piano performances. The identification is done
considering each feature individually as well as a fusion of the features.
Results show that the proposed method achieved a precision of 0.903 using
fusion features. Moreover, the onset time deviation feature shows promising
result when considered individually.
