High-generalization spoofing fingerprint detection based on commonality feature learning
Objective The realm of our daily lives has witnessed the ubiquitous integration of fingerprint recognition tech-nology in domains,such as authorized identification,fingerprint-based payments,and access control systems.However,recent studies have revealed the vulnerability of these systems to spoofing fingerprint attacks.Attackers can deceive authen-tication systems by imitating fingerprints using artificial materials.Thus,the authenticity of fingerprint under scrutiny must be ascertained prior to its use to authenticate the user's identity.The development of a spoofing fingerprint detection tech-nology has attracted extensive attention from the academia and industry.The creation of spoofing fingerprints involve the use of diverse materials.The present research disregards the correlation of data distribution among spoofing fingerprints crafted from various materials,which consequently leads to limited generalization in cross-material detection.Hence,a high-generalization spoofing fingerprint detection method based on commonality feature learning is proposed through the analysis of the distribution correlation among counterfeit fingerprint data originating from diverse materials and the explora-tion of invariant forgery features within the material domain of distinct counterfeit fingerprints.Method First,to character-ize and learn the features of spoofing fingerprints obtained using various materials,a multiscale spoofing feature extractor(MFSE)is designed,and it includes a multiscale spatial-channel attention module to allow the MFSE to pay more attention to fine-grained differences between live and fake fingerprints and improve the capability of the network to learn spoofing fea-tures.Then,a common spoofing feature extractor(CSFE)is constructed for further analysis of the distribution correlation between spoofing fingerprint data of different materials and extraction of common spoofing features between spoofing finger-prints made from various materials.Under the guidance of prior knowledge on MFSE,CSFE calculates the distance of the feature distribution extracted by MFSE and CSFE in the regenerated Hilbert space through the feature distance measure-ment module and minimizes the maximum mean difference(MMD)of data distributions to reduce the distance between them.The multitask material domain invariant spoofing feature learning is implemented,and a material discriminator is designed to constrain the learned common spoofing features and remove specific material information from the spoofing fin-gerprint.CSFE involves the calculation of multiple loss functions.Manually setting the weight ratio of these loss functions may prevent the improvement of model performance.Therefore,an adaptive joint-optimization loss function is used to bal-ance the loss values of each module and further expand the generalization capability of the network in the presence of unknown material spoofing fingerprints.The training process involves the use of a fingerprint image containing two kinds of labels,which include the authenticity label of the fingerprint and material label of the forged fingerprint.The true finger-print lacks material properties and is marked as 0.Forged fingerprints are numbered from 1 based on the material category,and the authenticity of fingerprints and type of forged materials are assessed based on the authenticity and material labels,respectively.The random gradient descent method is used for optimization,and the learning rate setting is from 0.001,which is reduced by 0.1 time per 10 epoch.Result The experimental results on two public datasets revealed that the algo-rithm proposed in this paper achieved the best comprehensive performance in the cross-material detection of forged finger-prints.On the GreenBit sensor of LivDet2017 dataset,average classification error(ACE)reduced the rate by 0.16％com-pared with the second-ranked spoofing fingerprint detection model and increased true detection rate(TDR)by 2.4％.On the Digital persona sensor of LivDet2017 dataset,ACE reduced the rate by 0.26％compared with the second-ranked forg-ery fingerprint detection model and increased TDR by 0.7％.On LivDet2019 dataset,ACE reduces the rate by 1.34％on average compared with the second-ranked spoofing fingerprint detection model and increases TDR by 1.43％on average.These findings indicate a an increase in the corresponding generalization.A comparative experiment was performed to verify the superiority of the multi-scale spatial-channel(MSC)attention module to the convolutional block attention module(CBAM)module in spoofing fingerprint detection.To better evaluate our method,we conducted a series of ablation experi-ments to verify each module involved in common feature extraction training to aid in the cross-material spoofing fingerprint detection task.To reveal the improved generalization performance of CSFE compared with MFSE in cross-material spoofing fingerprint detection,this paper visualized the distribution of the proposed features using the t-distributed stochastic neigh-bor embedding algorithm.Conclusion The method proposed in this paper achieved better detection results than other meth-ods and exhibited a higher generalization performance in the detection of spoofing fingerprints made of unknown materials.Compared with spoofing fingerprint detection using the same material,the extant spoofing fingerprint detection technique harbors substantial scope for the refinement of its generalization capabilities for cross-material detection.Cross-material spoofing fingerprint detection aptly aligns with practical requirements and bears immense importance in the realm of research pursuits.
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