检索结果-内蒙古大学图书馆

elephant herding with whale optimization enabled ORB features and CNN for Iris recognition

MULTIMEDIA TOOLS AND APPLICATIONS 2022年第4期81卷 5761-5794页

作者： Babu, Gorla Khayum, Pinjari Abdul JNTU Elect & Commun Engn Anantapur Andhra Pradesh India G Pullareddy Engn Coll Elect & Commun Engn Kurnool India

Iris biometrics is one of the frequently used biometrics for security purposes, and it provides the appropriate tools for identifying humans. Deep learners when used along with iris recognition can improve accuracy, automatic learning, and generalization ability. Despite the advantages of deep learners, some problems like time complexity and computational efforts exist in deep learners. In order to solve these obstacles, this paper intends to develop the intelligent iris recognition model based on the concepts merged with multi-objective feature selection and deep learning. The main phases of the proposed model involve (a) Pre-processing, (b) Iris Segmentation, (c) optimal ORB point selection and feature vector generation, and (d) optimal recognition. Here, the pre-processing of the image is performed by filtering and contrast enhancement techniques. Further, the iris segmentation is done by few approaches such as reflection moving, iris localization, and Hough-transform-based segmentation. Once the segmentation of the iris is finished, feature extraction is carried out by optimized Oriented FAST and rotated BRIEF (ORB) features concerning on multi-objective function. Finally, the optimized Convolutional Neural Network (CNN) is used for iris recognition. Here, the hybrid algorithm with the integration of two meta-heuristic algorithms like elephant herding optimization (EHO), and whale optimization algorithm (WOA) called elephant herding with whale optimization algorithm (EH-WOA) is utilized for performing the optimized ORB feature selection and optimized CNN. The experiments are conducted on a benchmark datasets like IIT Delhi (IITD) Iris database, and MMU iris dataset to analyze the performance of the proposed model over the different network structures for accurate iris recognition. Through the experimental analysis, the proposed optimized CNN + EH-WOA had better values in terms of accuracy, which is 0.98% higher than SVM, 0.92% higher than KNN, 0.88% higher NN, a

关键词： Iris recognition Iris segmentation ORB point selection Convolutional neural network elephant herding with whale optimization algorithm Multi-objective function

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：