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19 October 2022 Research on face recognition based on residual neural network
Jiaqi Feng
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Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122943H (2022) https://doi.org/10.1117/12.2639753
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China
Abstract
The application scenarios of face recognition are very wide, and it has become a research hotspot in the field of computer vision. The deep learning technology represented by the convolutional neural network has shown great advantages in the field of face recognition. Its network structure is developing in a deeper and wider direction, and has achieved better and better recognition results. Due to its complex structure, the training is difficult to converge and occupies a large amount of memory and takes a long time to load, which is not conducive to the application in practical engineering. In order to solve this problem, this paper studies the application of deep learning in the field of face recognition, and proposes an improved residual neural network. On the basis of the original model, this model sacrifices less accuracy, and improves the speed of face recognition to a greater extent. This makes the model of great significance in the field of engineering practice.
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Jiaqi Feng "Research on face recognition based on residual neural network", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122943H (19 October 2022); https://doi.org/10.1117/12.2639753
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KEYWORDS
Facial recognition systems
Convolution
Data modeling
Neural networks
RGB color model
Performance modeling
Image processing
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