  Rotation moment invariants have been of great interest in image processing
and pattern recognition. This paper presents a novel kind of rotation moment
invariants based on the Slepian functions, which were originally introduced in
the method of separation of variables for Helmholtz equations. They were first
proposed for time series by Slepian and his coworkers in the 1960s. Recent
studies have shown that these functions have an good performance in local
approximation compared to other approximation basis. Motivated by the good
approximation performance, we construct the Slepian-based moments and derive
the rotation invariant. We not only theoretically prove the invariance, but
also discuss the experiments on real data. The proposed rotation invariants are
robust to noise and yield decent performance in facial expression
classification.
