基于改进浣熊优化算法的多模态生物特征识别  

Multimodal Biometric Recognition Based on Improved Coati Optimization Algorithm

作　　者：刘丰华 张琪[1] 王财勇 LIU Fenghua;ZHANG Qi;WANG Caiyong(School of Information and Cyber Security,People's Public Security University of China,Beijing 100038,China;School of Intelligence Science and Technology,Beijing University of Civil Engineering and Architecture,Beijing 100044,China)

机构地区：[1]中国人民公安大学,信息网络安全学院,北京100038 [2]北京建筑大学,智能科学与技术学院,北京100044

出　　处：《数据与计算发展前沿(中英文)》2025年第1期56-67,共12页Frontiers of Data & Computing

基　　金：国家自然科学基金(61906199,62106015)。

摘　　要：【目的】为了提升身份识别的安全性与准确性,本文提出了一个在分数层融合虹膜、人脸、眼周三个模态的生物特征识别算法。【方法】首先,该算法使用轻量级卷积神经网络作为特征提取器,计算特征向量间的余弦相似度作为不同对象之间的匹配得分;其次,使用佳点集初始化提升浣熊优化算法的种群多样性,在探索阶段加入莱维飞行来增强全局搜索能力,通过改进浣熊优化算法求解三个模态得分在预定义融合规则下的最优参数;最后,通过Sch-weizer算子对不同参数组合进行模糊推理后,使用最小隶属度法去模糊化,得到最优分数融合规则及其参数。【结果】从CASIA-IrisV4-Distance数据集中构造同源面部多模态数据集进行对比实验,实验结果表明,与基线模型相比,本算法的等错误率(EER)的值降低0.89%,错误匹配率(FMR)为10-5时错误非匹配率(FNMR)的值降低3.32%,区分性指标提升0.61;与四种优化算法相比,本算法的识别精度更高。【结论】由此可见,本文所提算法在多模态分数层融合中获得了良好的识别效果。[Objective]In order to improve the security and accuracy of biometric recognition technology,this paper proposes an algorithm that integrates three modalities of iris,face,and periocular at the score level.[Methods]Firstly,the algorithm uses a lightweight convolutional neural network as the feature extractor,which calculates the cosine similarity between feature vectors as the matching score between different objects.Secondly,the good point set initialization is used to enhance the population diversity of the Coati Optimization Algorithm.Levy flight is added in the exploration phase to improve the global search capability.The improved Coati Optimization Algorithm is used to solve the best parameters of the three modal scores under predefined fusion rules.Finally,the Schweizer operator is used to perform fuzzy inference on different parameter combinations,and the minimum membership degree method is used to defuzzify and obtain the optimal score fusion rules and their parameters.[Results]A homogenous facial multimodal dataset was constructed from the CASIA-IrisV4-Distance dataset for comparative experiments.The experimental results show that compared with the baseline model,the equal error rate(EER)decreases by 0.89%,the false mismatch rate(FNMR)decreases by 3.32%when the false matching rate(FMR)is 10-5,and the discriminative index improved by 0.61.Compared to four optimization algorithms,this algorithm has higher recognition accuracy.[Conclusions]It can be seen that the algorithm proposed in this paper has achieved good recognition performance in multimodal score layer fusion.

关 键 词：多模态融合 浣熊优化算法 生物特征识别 分数层融合 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]