ResNet and PCA-Based Deep Learning Scheme for Efficient Face Recognition
Rajendra Kumar Dwivedi and
Devesh Kumar
Additional contact information
Rajendra Kumar Dwivedi: Madan Mohan Malaviya University of Technology, India
Devesh Kumar: Babasaheb Bhimrao Ambedkar University, India
International Journal of Intelligent Information Technologies (IJIIT), 2023, vol. 19, issue 1, 1-20
Abstract:
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.
Date: 2023
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIIT.329957 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:igg:jiit00:v:19:y:2023:i:1:p:1-20
Access Statistics for this article
International Journal of Intelligent Information Technologies (IJIIT) is currently edited by Vijayan Sugumaran
More articles in International Journal of Intelligent Information Technologies (IJIIT) from IGI Global
Bibliographic data for series maintained by Journal Editor ().