A convolutional neural network face recognition algorithm based on bootstrapping

Wenhao Jiang; Zheng Li

doi:10.1117/12.3045854

16 January 2025 A convolutional neural network face recognition algorithm based on bootstrapping

Wenhao Jiang, Zheng Li

Author Affiliations +

Proceedings Volume 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024); 134474W (2025) https://doi.org/10.1117/12.3045854
Event: International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 2024, Wuhan, China

Abstract

Convolutional Neural Network models can be used to train a large amount of data for subsequent data analysis. This paper proposes a Convolutional Neural Network framework model based on Bootstrapping for Deep Learning and analysis of massive face data containing noise. According to the analysis of experimental results, it is concluded that the proposed Bootstrapping based Convolutional Neural Network framework model can achieve Deep Learning and subsequent prediction of large-scale noisy labeled facial data. It has the advantage of low computational cost and has achieved stateof- the-art results in various facial benchmark tests.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Wenhao Jiang and Zheng Li "A convolutional neural network face recognition algorithm based on bootstrapping", Proc. SPIE 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 134474W (16 January 2025); https://doi.org/10.1117/12.3045854

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KEYWORDS

Data modeling

Facial recognition systems

Deep learning

Education and training

Convolutional neural networks

Feature extraction

RGB color model

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