White shark optimizer via support vector machine for video-based gender classification system

作     者：Oyediran, Mayowa Oyedepo Ajagbe, Sunday Adeola Ojo, Olufemi Samuel Alshahrani, Reem Awodoye, Olufemi O. Adigun, Matthew O. 

作者机构：Department of Computer Engineering Ajayi Crowther University Oyo Nigeria Department of Computer Science University of Zululand Kwadlangezwa3886 South Africa Department of Computer Engineering Abiola Ajimobi Technical University Ibadan200255 Nigeria Department of Computer Science College of Computers and IT Taif University P.O.Box 11099 Taif21944 Saudi Arabia Department of Computer Engineering Ladoke Akintola University of Technology Ogbomoso Nigeria 

出 版 物：《Multimedia Tools and Applications》 (Multimedia Tools Appl)

年 卷 期：2025年

页      面：1-17页

核心收录：

学科分类：0303[法学-社会学] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Open access funding provided by University of Zululand. This research was funded by Taif University  Taif  Saudi Arabia (TU-DSPP-2024-291) 

主　　题：Support vector machines 

摘      要：Gender identification from videos is a challenging task with significant real-world applications, such as video content analysis and social behavior research. In this study, we propose a novel approach, the White Shark Optimizer-Support Vector Machine (WSO-SVM), tailored specifically for gender identification from video data. The WSO-SVM integrates the White Shark Optimizer, a bio-inspired optimization algorithm mimicking the hunting behavior of white sharks, with the Support Vector Machine, a powerful machine learning technique for classification. By combining these two methods, we aim to exploit the advantages of both algorithms and enhance gender identification accuracy. To evaluate the performance of the WSO-SVM in gender identification, the work conducted extensive experiments using a diverse dataset of video clips containing individuals of various genders and backgrounds. The work compared the results with conventional SVM-based gender identification and state-of-the-art methods. The findings demonstrate that the WSO-SVM achieves superior accuracy in gender identification compared to traditional SVM-based approaches. The WSO-SVM s ability to efficiently explore the solution space and select optimal SVM parameters contributes to its improved performance. Moreover, the WSO-SVM exhibits robustness in handling variations in lighting conditions, poses, and facial expressions, making it well-suited for real-world video-based gender identification tasks. The outcomes derived from the SVM approach demonstrate that WSO-SVM produced an average FPR of 7.14%, Sensitivity of 93.06%, Specificity of 92.86%, Precision of 91.0%, and overall accuracy of 93.00% in 45.83 s with a recognition time of 45.83 s. © The Author(s) 2025.