基于ResNet和PCA的高效人脸识别深度学习方案

IF 0.6 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Intelligent Information Technologies Pub Date : 2023-09-06 DOI:10.4018/ijiit.329957

Rajendra Kumar Dwivedi, Devesh Kumar

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引用次数: 0

摘要

人脸识别是近年来一个新兴的研究领域。随着深度学习的兴起，人脸识别变得高效和精确，创造了新的里程碑。通过设计新的方案，可以提高现有方案的性能、精度和计算时间。在此背景下，本文提出了基于残差网络(ResNet)和基于Dlib库的深度学习主成分分析(PCA)方案的人脸识别多类分类框架。使用主成分分析，该框架的人脸识别准确率达到99.6%，计算时间减少68.03%。

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ResNet and PCA-Based Deep Learning Scheme for Efficient Face Recognition

Face recognition is an emerging field of research in recent days. With the rise of deep learning, face recognition has become efficient and precise, creating new milestones. The performance, accuracy, and computational time of the existing schemes can be enhanced by devising a new scheme. In this context, a multiclass classification framework for face recognition using residual network (ResNet) and principal component analysis (PCA) schemes of deep learning with Dlib library is proposed in this paper. The proposed framework produces face recognition accuracy of 99.6% and a reduction of computational time with 68.03% using principal component analysis.

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来源期刊

International Journal of Intelligent Information Technologies COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

2.50

自引率

0.00%

发文量

期刊介绍： The International Journal of Intelligent Information Technologies (IJIIT) encourages quality research dealing with (but not limited to) the following topics: •Agent-based auction, contracting, negotiation, and ecommerce •Agent-based control and supply chain •Agent-based simulation and application integration •Cooperative and collaborative systems •Distributed intelligent systems and technologies •Human-agent interaction and experimental evaluation •Implementation, deployment, diffusion, and organizational impact •Integrating business intelligence from internal and external sources •Intelligent agent and multi-agent systems in various domains •Intelligent decision support systems