An Interpretable Siamese Attention Res-CNN for Fingerprint Spoofing Detection
In recent years, fingerprint authentication has gained widespread adoption in diverse identification systems, including smartphones, wearable devices, and attendance machines, etc. Nonetheless, these systems are vulnerable to spoofing attacks from suspicious fingerprints, posing significant risks to...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | IET Biometrics |
| Online Access: | http://dx.doi.org/10.1049/2024/6630173 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850210658586460160 |
|---|---|
| author | Chengsheng Yuan Zhenyu Xu Xinting Li Zhili Zhou Junhao Huang Ping Guo |
| author_facet | Chengsheng Yuan Zhenyu Xu Xinting Li Zhili Zhou Junhao Huang Ping Guo |
| author_sort | Chengsheng Yuan |
| collection | DOAJ |
| description | In recent years, fingerprint authentication has gained widespread adoption in diverse identification systems, including smartphones, wearable devices, and attendance machines, etc. Nonetheless, these systems are vulnerable to spoofing attacks from suspicious fingerprints, posing significant risks to privacy. Consequently, a fingerprint presentation attack detection (PAD) strategy is proposed to ensure the security of these systems. Most of the previous work concentrated on how to build a deep learning framework to improve the PAD performance by augmenting fingerprint samples, and little attention has been paid to the fundamental difference between live and fake fingerprints to optimize feature extractors. This paper proposes a new fingerprint liveness detection method based on Siamese attention residual convolutional neural network (Res-CNN) that offers an interpretative perspective to this challenge. To leverage the variance in ridge continuity features (RCFs) between live and fake fingerprints, a Gabor filter is utilized to enhance the texture details of the fingerprint ridges, followed by the construction of an attention Res-CNN model to extract RCF between the live and fake fingerprints. The model mitigates the performance deterioration caused by gradient disappearance. Furthermore, to highlight the difference in RCF, a Siamese attention residual network is devised, and the ridge continuity amplification loss function is designed to optimize the training process. Ultimately, the RCF parameters are transferred to the model, and transfer learning is utilized to aid its acquisition, thereby assuring the model’s interpretability. The experimental outcomes conducted on three publicly accessible fingerprint datasets demonstrate the superiority of the proposed method, exhibiting remarkable performance in both true detection rate and average classification error rate. Moreover, our method exhibits remarkable capabilities in PAD tasks, including cross-material experiments and cross-sensor experiments. Additionally, we leverage Gradient-weighted Class Activation Mapping to generate a heatmap that visualizes the interpretability of our model, offering a compelling visual validation. |
| format | Article |
| id | doaj-art-90a2dbfd7657486e86a31e91b69f45bf |
| institution | OA Journals |
| issn | 2047-4946 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-90a2dbfd7657486e86a31e91b69f45bf2025-08-20T02:09:44ZengWileyIET Biometrics2047-49462024-01-01202410.1049/2024/6630173An Interpretable Siamese Attention Res-CNN for Fingerprint Spoofing DetectionChengsheng Yuan0Zhenyu Xu1Xinting Li2Zhili Zhou3Junhao Huang4Ping Guo5School of Computer ScienceSchool of Computer ScienceCollege of Foreign LanguagesInstitute of Artificial Intelligence and BlockchainInstitute of Artificial Intelligence and BlockchainSchool of Computer ScienceIn recent years, fingerprint authentication has gained widespread adoption in diverse identification systems, including smartphones, wearable devices, and attendance machines, etc. Nonetheless, these systems are vulnerable to spoofing attacks from suspicious fingerprints, posing significant risks to privacy. Consequently, a fingerprint presentation attack detection (PAD) strategy is proposed to ensure the security of these systems. Most of the previous work concentrated on how to build a deep learning framework to improve the PAD performance by augmenting fingerprint samples, and little attention has been paid to the fundamental difference between live and fake fingerprints to optimize feature extractors. This paper proposes a new fingerprint liveness detection method based on Siamese attention residual convolutional neural network (Res-CNN) that offers an interpretative perspective to this challenge. To leverage the variance in ridge continuity features (RCFs) between live and fake fingerprints, a Gabor filter is utilized to enhance the texture details of the fingerprint ridges, followed by the construction of an attention Res-CNN model to extract RCF between the live and fake fingerprints. The model mitigates the performance deterioration caused by gradient disappearance. Furthermore, to highlight the difference in RCF, a Siamese attention residual network is devised, and the ridge continuity amplification loss function is designed to optimize the training process. Ultimately, the RCF parameters are transferred to the model, and transfer learning is utilized to aid its acquisition, thereby assuring the model’s interpretability. The experimental outcomes conducted on three publicly accessible fingerprint datasets demonstrate the superiority of the proposed method, exhibiting remarkable performance in both true detection rate and average classification error rate. Moreover, our method exhibits remarkable capabilities in PAD tasks, including cross-material experiments and cross-sensor experiments. Additionally, we leverage Gradient-weighted Class Activation Mapping to generate a heatmap that visualizes the interpretability of our model, offering a compelling visual validation.http://dx.doi.org/10.1049/2024/6630173 |
| spellingShingle | Chengsheng Yuan Zhenyu Xu Xinting Li Zhili Zhou Junhao Huang Ping Guo An Interpretable Siamese Attention Res-CNN for Fingerprint Spoofing Detection IET Biometrics |
| title | An Interpretable Siamese Attention Res-CNN for Fingerprint Spoofing Detection |
| title_full | An Interpretable Siamese Attention Res-CNN for Fingerprint Spoofing Detection |
| title_fullStr | An Interpretable Siamese Attention Res-CNN for Fingerprint Spoofing Detection |
| title_full_unstemmed | An Interpretable Siamese Attention Res-CNN for Fingerprint Spoofing Detection |
| title_short | An Interpretable Siamese Attention Res-CNN for Fingerprint Spoofing Detection |
| title_sort | interpretable siamese attention res cnn for fingerprint spoofing detection |
| url | http://dx.doi.org/10.1049/2024/6630173 |
| work_keys_str_mv | AT chengshengyuan aninterpretablesiameseattentionrescnnforfingerprintspoofingdetection AT zhenyuxu aninterpretablesiameseattentionrescnnforfingerprintspoofingdetection AT xintingli aninterpretablesiameseattentionrescnnforfingerprintspoofingdetection AT zhilizhou aninterpretablesiameseattentionrescnnforfingerprintspoofingdetection AT junhaohuang aninterpretablesiameseattentionrescnnforfingerprintspoofingdetection AT pingguo aninterpretablesiameseattentionrescnnforfingerprintspoofingdetection AT chengshengyuan interpretablesiameseattentionrescnnforfingerprintspoofingdetection AT zhenyuxu interpretablesiameseattentionrescnnforfingerprintspoofingdetection AT xintingli interpretablesiameseattentionrescnnforfingerprintspoofingdetection AT zhilizhou interpretablesiameseattentionrescnnforfingerprintspoofingdetection AT junhaohuang interpretablesiameseattentionrescnnforfingerprintspoofingdetection AT pingguo interpretablesiameseattentionrescnnforfingerprintspoofingdetection |