改进麻雀搜索算法优化支持向量机的人脸识别

Face Recognition Based on SVM Optimized by Improved Sparrow Search Algorithm

为提升人脸识别的准确性,提出一种融合Tent混沌映射的麻雀搜索算法优化支持向量机参数的分类模型。首先改进麻雀搜索算法,选择Tent映射初始化麻雀种群,提高发现者种群质量;然后加入自适应调整惯性权重策略,增强全局搜索能力与收敛速度;接下来添加柯西变异对适应度较好的个体进行突变,解决算法停滞问题;最后将改进后的麻雀搜索算法用于优化支持向量机的核参数与惩罚参数,动态调整人脸相似度的接受阈值,实现错误分类率评价指标数值的最小化。结果显示,该方法在人脸识别分类中的准确率达到98.5%。

In order to improve the accuracy of face recognition,a classification model of the sparrow search algorithm that integrates the chaot‐ic mapping of Tent is proposed.First of all,improve the sparrow search algorithm,select the test mapping initialization of the sparrow popula‐tion to improve the quality of the founder's population;add the adaptive adjustment of the inertial weight strategy,enhance the global search ability and convergence speed;Solve the problem of stagnation of algorithms.Then use the improved sparrow search algorithm to adjust the nu‐clear parameters and punishment parameters of the support vector machine,dynamically adjust the acceptance threshold of the face similarity,and minimize the value of the error classification rate evaluation index.The results show that this method has an accuracy rate of 98.5%in the face recognition classification.