Facial Expression Classification Using Rotation Slepian-based Moment Invariants
This work addresses facial expression recognition, a domain-specific problem, with an incremental improvement using a novel method for known bottlenecks.
The paper tackled facial expression classification by constructing rotation moment invariants based on Slepian functions, which proved robust to noise and achieved decent performance in experiments on real data.
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.