Face Recognition Algorithm Based on Improved Artificial Hummingbird Algorithm-Optimized Support Vector Machine

doi:10.19678/j.issn.1000-3428.0069182

Abstract

Abstract:

Traditional face recognition systems use various bionic algorithms combined with Support Vector Machines (SVM) to form a corresponding face recognition model for the final face classification problem. This method selects the optimal SVM parameters through algorithm iteration. However, this strategy is hindered by low classification accuracy, long training time, and the possibility of easily falling into the local optimal solution. This paper proposes a face recognition method using an improved Artificial Hummingbird Algorithm (AHA) to optimize SVM. First, AHA is improved by introducing a chaotic sequence of Tent mapping so that the hummingbird population is initialized more uniformly and the algorithm does not fall into the local optimal solution; second, the improved AHA algorithm is introduced in the method of face recognition using SVM. By setting a certain number of iterations for the algorithm, the optimal relevant parameters used to optimize SVM are selected to improve face recognition accuracy. The improved AHA is compared to the Grey Wolf Optimizer (GWO), Sparrow Search Algorithm (SSA), and Whale Optimization Algorithm (WOA). The improved AHA has a faster convergence speed in solving the benchmark function. Simultaneously, in a face recognition experiment on the ORL face database, the improved AHA combined with SVM is compared to GWO, SSA and WOA combined with SVM. In face recognition tasks, the improved AHA combined with SVM achieves higher accuracy and recall rate, with a faster inference speed.

Key words: Artificial Hummingbird Algorithm (AHA), Support Vector Machines (SVM), face recognition, Tent mapping, chaotic sequence

摘要：

传统的人脸识别系统在最终人脸分类问题上，通常借助各种仿生学算法与支持向量机(SVM)相结合组成相应的人脸识别模型。该方法通过算法的迭代选取最优SVM参数，然而这种策略在人脸识别方法上存在分类精度较低、训练时间较长且容易陷入局部最优解的问题。针对上述问题，提出利用改进人工蜂鸟算法(AHA)优化SVM的人脸识别算法。首先通过引入Tent映射的混沌序列改进人工蜂鸟算法，使蜂鸟种群初始化更为均匀，避免算法陷入局部最优解；其次在SVM进行人脸识别的方法中引入改进AHA，通过设定一定的迭代次数，选择用来优化SVM的最优相关参数，达到提高人脸识别准确率的目的。实验结果表明，将改进的人工蜂鸟算法与灰狼优化(GWO)算法、麻雀搜索算法(SSA)、鲸鱼优化算法(WOA)进行对比，改进AHA在基准函数的求解上具有更快的收敛速度, 同时在ORL人脸数据库进行人脸识别实验，将改进AHA与SVM相结合，相比于将GWO、SSA和WOA与SVM相结合，在人脸识别的准确率指标方面，改进AHA结合SVM方案具有更高的准确率和召回率，并且模型推理速度更快。

关键词: 人工蜂鸟算法, 支持向量机, 人脸识别, Tent映射, 混沌序列

XIAO Jian, HUANG Bo, CHENG Hongliang, HU Xin, YUAN Ye. Face Recognition Algorithm Based on Improved Artificial Hummingbird Algorithm-Optimized Support Vector Machine[J]. Computer Engineering, 2025, 51(10): 319-326.

肖剑, 黄博, 程鸿亮, 胡欣, 袁晔. 基于改进人工蜂鸟算法优化支持向量机的人脸识别算法[J]. 计算机工程, 2025, 51(10): 319-326.

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Figures/Tables 12

Fig.1 Principle of support vector machine

Fig.2 Initialization of the original hummingbird food source location

Fig.3 Initialization of the improve hummingbird food source location

Fig.4 Hummingbird flight skills

Fig.5 Comparison of convergence curves of benchmark functions

Fig.6 Improved AHA-SVM accuracy

Fig.7 GWO-SVM accuracy

Fig.8 SSA-SVM accuracy

Fig.9 WOA-SVM accuracy

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