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Rotation moment invariants have been of great interest in image processingand pattern recognition. This paper presents a novel kind of rotation momentinvariants based on the Slepian functions, which were originally introduced inthe method of separation of variables for Helmholtz equations. They were firstproposed for time series by Slepian and his coworkers in the 1960s. Recentstudies have shown that these functions have an good performance in localapproximation compared to other approximation basis. Motivated by the goodapproximation performance, we construct the Slepian-based moments and derivethe rotation invariant. We not only theoretically prove the invariance, butalso discuss the experiments on real data. The proposed rotation invariants arerobust to noise and yield decent performance in facial expressionclassification.

Facial Expression Classification Using Rotation Slepian-based Moment Invariants