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22 April 2022 Analyzing network architecture models based on applications
Shenyi Jiang
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Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121631B (2022) https://doi.org/10.1117/12.2627811
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China
Abstract
Nowadays, under the background of the global pandemic, people are required to wear face masks. Thus, it is harder to identify a person because features such as mouths and noses are being covered. Discovering a measure to recognize the identity rapidly is regarded as one of the top urgent priorities. Fortunately, thanks to the boost of various network architecture models, the problem is on the way of solving. Actually, the network architectures pose positive impacts not only on facial recognition, but also be beneficial to all aspects of daily life, like image semantic segmentation and object detection, which can be used to divide the goal and background information. The paper mainly analyzes the differences between network architectures LeNet, AlexNet and VGG, including advantages and drawbacks, and provides possible applications of models.
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Shenyi Jiang "Analyzing network architecture models based on applications", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121631B (22 April 2022); https://doi.org/10.1117/12.2627811
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KEYWORDS
Image segmentation
Network architectures
Facial recognition systems
RGB color model
Convolution
Image processing
Visualization
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