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

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  • Rajendra Kumar Dwivedi

    (Madan Mohan Malaviya University of Technology, India)

  • Devesh Kumar

    (Babasaheb Bhimrao Ambedkar University, India)

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.

Suggested Citation

  • Rajendra Kumar Dwivedi & Devesh Kumar, 2023. "ResNet and PCA-Based Deep Learning Scheme for Efficient Face Recognition," International Journal of Intelligent Information Technologies (IJIIT), IGI Global, vol. 19(1), pages 1-20, January.
    • Handle: RePEc:igg:jiit00:v:19:y:2023:i:1:p:1-20
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