Face recognition technology based on ResNet-50

Jingyu Liu

doi:10.54254/2755-2721/39/20230593

Research Article

Open access

Published on 21 February 2024

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Liu,J. (2024). Face recognition technology based on ResNet-50. Applied and Computational Engineering,39,160-165.

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Face recognition technology based on ResNet-50

Jingyu Liu *^,1,

¹ University of Macau

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/39/20230593

Abstract

Face recognition technology is progressively finding its place across diverse domains. In pursuit of enhancing the efficacy of face recognition systems, this study employs a ResNet-50 deep convolutional neural network. The dataset is meticulously gathered and processed via OpenCV, thus amplifying the precision and utility of face recognition. ResNet, an advanced convolutional neural network, incorporates the concept of residual connections, bridging convolutional layers through shortcut connections. These connections facilitate the addition of input to output, forming residual blocks. Consequently, ResNet-50 efficiently tackles the vanishing gradient issue, enabling the training of exceptionally deep networks. With 49 convolutional layers and a fully connected layer, ResNet-50 boasts a robust architecture. To emulate varying brightness conditions, post-collection image adjustments are applied randomly. This strategy curbs the impact of divergent lighting scenarios on recognition accuracy, bolstering the model’s practical applicability. Notably, experimental outcomes underscore the commendable performance of the trained ResNet-50 model in face recognition trials. This substantiates the broad-spectrum viability of face recognition technology in domains such as security surveillance, human-machine interaction, identity verification, and beyond.

Keywords

ResNet-50, Face Recognition, Deep Convolutional Neural Networks, Deep Learning

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Cite this article

Liu,J. (2024). Face recognition technology based on ResNet-50. Applied and Computational Engineering,39,160-165.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

Conference website: https://2023.confmla.org/

ISBN：978-1-83558-303-6(Print) / 978-1-83558-304-3(Online)

Conference date: 18 October 2023

Editor：Mustafa İSTANBULLU

Series: Applied and Computational Engineering

Volume number: Vol.39

ISSN：2755-2721(Print) / 2755-273X(Online)

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