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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >Face Recognition Based on SVM Optimized by the Improved Bacterial Foraging Optimization Algorithm
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Face Recognition Based on SVM Optimized by the Improved Bacterial Foraging Optimization Algorithm

机译：基于改进的细菌觅食优化算法优化SVM的人脸识别

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摘要

Support vector machine (SVM) is always used for face recognition. However, kernel function selection (kernel selection and its parameters selection) is a key problem for SVMs, and it is difficult. This paper tries to make some contributions to this problem with focus on optimizing the parameters in the selected kernel function. Bacterial foraging optimization algorithm, inspired by the social foraging behavior of Escherichia coli, has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. Therefore, we proposed to optimize the parameters in SVM by an improved bacterial foraging optimization algorithm (IBFOA). In the improved version of bacterial foraging optimization algorithm, a dynamical elimination-dispersal probability in the elimination-dispersal step and a dynamical step size in the chemotactic step are used to improve the performance of bacterial foraging optimization algorithm. Then the optimized SVM is used for face recognition. Simultaneously, an improved local binary pattern is proposed to extract features of face images in this paper to improve the accuracy rate of face recognition. Numerical results show the advantage of our algorithm over a range of existing algorithms.
机译：支持向量机（SVM）始终用于面部识别。但是，内核函数选择（内核选择及其参数选择）是SVM的关键问题，很难。本文试图对此问题进行一些贡献，重点是优化所选内核功能中的参数。受到大肠杆菌的社会觅食行为的灵感的细菌觅食优化算法已被广泛接受作为分布式优化和控制的当前兴趣的全局优化算法。因此，我们提出通过改进的细菌觅食优化算法（IBFOA）来优化SVM中的参数。在细菌觅食优化算法的改进版本中，在消除分散步骤中动态消除分散概率和趋化学步骤中的动力学阶梯尺寸用于改善细菌觅食优化算法的性能。然后优化的SVM用于面部识别。同时，提出了一种改进的局部二进制图案以提取本文中的面部图像的特征，以提高面部识别的精度率。数值结果显示了我们算法在一系列现有算法中的优势。

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