[1]ZHAO Dongyue,SHI Lei,DING Meng.Fingerprint pattern classification based on dual-branch attention mechanism[J].CAAI Transactions on Intelligent Systems,2025,20(4):936-945.[doi:10.11992/tis.202407005]
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Fingerprint pattern classification based on dual-branch attention mechanism

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 4 Page number: 936-945 Column: 学术论文—机器学习 Public date: 2025-08-05
Title:
Fingerprint pattern classification based on dual-branch attention mechanism
Author(s):
ZHAO Dongyue1;  SHI Lei2;  DING Meng1; 3
1. Department of Criminal Investigation, People’s Public Security University of China, Beijing 100038, China;
2. State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China;
3. Public Security Behavioral Science Lab, People’s Public Security University of China, Beijing 100038, China
Keywords:
image processing; fingerprint classification; dual-branch structure; attention mechanism; feature fusion; hyperparameter; activation function; deep learning
CLC:
TP391.4
DOI:
10.11992/tis.202407005
Abstract:
In order to address the challenges of low-quality fingerprint recognition, insufficient feature extraction and information loss during the extraction process in existing fingerprint classification algorithms, a novel fingerprint pattern classification algorithm based on a dual-branch attention mechanism is proposed. The algorithm employs a dual-branch network for the extraction of orientation fields, which are then subjected to Gabor filtering for the purpose of feature fusion. This approach allows for effective utilisation of both ridge features and global features inherent to fingerprint images. A combination of activation functions and a comprehensive attention mechanism module are proposed to effectively extract spatial and channel feature information from the convolutional branch, thereby reducing information loss during feature extraction. A branch feature fusion module has been devised with the objective of weighting and integrating the feature maps that are output by the dual branches, thereby ensuring comprehensive feature fusion. Finally, an improved cross-entropy loss function is introduced to mitigate the problem of imbalanced sample distribution. The results of the experimental study demonstrate that the proposed algorithm achieves a 99.08% accuracy rate in the classification of four types of fingerprints on a self-built fingerprint pattern dataset, outperforming other network models in terms of accuracy, F1 score, and area under curve. These findings verify the effectiveness and superiority of the algorithm in fingerprint pattern classification tasks.
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