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Deep Learning Algorithms for Efficient Recognition in Biometric Image Classification

Jing Ning

Jing Ning

School of Information Engineering, Liaodong UniversityDandong, China
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Ning, Jing
  
Mar 17, 2025
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Published Online: Mar 17, 2025
Received: Oct 27, 2024
Accepted: Feb 09, 2025
DOI: https://doi.org/10.2478/amns-2025-0163
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© 2025 Jing Ning, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Example of network structure
Example of network structure

Figure 2.

He convolution network and optimized elm classification algorithm
He convolution network and optimized elm classification algorithm

Figure 3.

Model accuracy curve
Model accuracy curve

Figure 4.

Model loss curve
Model loss curve

Figure 5.

Various evaluation results of different models
Various evaluation results of different models

Figure 6.

Roc curve of different models
Roc curve of different models

Figure 7.

The confusion matrix of the densenet121 model
The confusion matrix of the densenet121 model

Figure 8.

Classification accuracy
Classification accuracy

Performance comparison of different models

Model name MACs(G) Parameter quantity(M) Speed(ms)
ResNet50 0.27 24.56 39.56
ResNeXt50 0.31 23.98 34.51
Efficient Net 0.15 2.56 31.28
DenseNet121 0.42 21.02 28.14
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Journal Subjects:
Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other
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