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Multidimensional Bernstein polynomials and Bezier curves: Analysis of machine learning algorithm for facial expression recognition based on curvature

机译:多维伯恩斯坦多项式和Bezier曲线:基于曲率的面部表情识别机器学习算法分析

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摘要

In this paper, by using partial derivative formulas of generating functions for the multidimensional unification of the Bernstein basis functions and their functional equations, we derive derivative formulas and identities for these basis functions and their generating functions. We also give a conjecture and some open questions related to not only subdivision property of these basis functions, but also solutions of a higher-order special differential equations. Moreover, we provide an implementation for a real world problem of human facial expression recognition with the help of curvature of Bezier curves whose machine learning supported by statistical evaluations on feature vectors using in the aforementioned machine learning algorithm.
机译:在本文中,通过使用伯恩斯坦基本函数的多维统一的局部衍生公式及其功能方程,我们导出了这些基本函数的衍生公式和标识。 我们还提供了与这些基本函数的细分特性相关的猜想和一些打开的问题,也是高阶特殊微分方程的解决方案。 此外,我们提供了利用前述机器学习算法的特征向量的统计评估支持的Bezier曲线的曲率来实现人类面部表情识别的真实世界问题的实现。

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