基于旋转Slepian不变矩的人脸表情分类
Facial Expression Classification Using Rotation Slepian-based Moment Invariants
摘要：
旋转不变矩在图像处理和模式识别中具有重要的应用价值。本文提出了一种新的基于Slepian函数的旋转矩不变量，它最初是在Helmholtz方程的分离变量方法中引入的。它们最早是由Slepian和他的同事在20世纪60年代提出的。最近的研究表明，这些函数具有良好的性能，在局部逼近相比，其他的近似基。基于其良好的逼近性能，我们构造了基于Slepian的矩，并推导出旋转不变量。我们不仅从理论上证明了这种不变性，而且讨论了实际数据的实验。所提出的旋转不变量对噪声具有很好的鲁棒性，在人脸表情分类中具有良好的性能。
英文摘要：
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.
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