FLD-SRC: Fingerprint Liveness Detection for AFIS Based on Spatial Ridges Continuity

• Published in: IEEE journal of selected topics in signal processing 2022-06, Vol.16 (4), p.817-827
• Main Authors: Yuan, Chengsheng, Yu, Peipeng, Xia, Zhihua, Sun, Xingming, Wu, Q. M. Jonathan
• Format: Article
• Language: English
• Subjects: AFIS; Artificial neural networks; Correlation; Counterfeit; Fabrication; Feature extraction; Feature maps; Fingerprinting; Fingerprints; FLD; GRU; Hardware; intra-continuity; pruning; Ridges; Rough surfaces; Spoofing; Surface roughness; Surface treatment
• ISSN: 1932-4553; 1941-0484
• DOI: 10.1109/JSTSP.2022.3174655

Automatic fingerprint identification system (AFIS) uses fingerprint to authenticate users, which is legal if the user is enrolled. However, numerous studies reveal that it is susceptible to spoofing attacks where a third person might freely synthesize counterfeit fingerprints to trick the scanner. To resist spoofing attacks, it makes fingerprint liveness detection (FLD) highly desirable. Most of previous work was to directly input the whole fingerprints into convolutional neural network, making it impossible to fully explore the relationship of spatial ridges, especially those with the latent fine-grained minutia on fingerprint ridges. Accordingly, in this paper, we exploit the relationship of spatial ridges in fingerprints and propose a novel FLD method based on spatial ridges continuity (FLD-SRC). Several fingerprint patches are first selected utilizing ridge texture saturation, and then uniformly split into several slices and thus construct the spatial continuity between pixels and between slices. Next, the proposed FLD-SRC learns deep features from fingerprints and eliminates redundant information. After that, the extracted feature maps are treated as a sequence and analyzed the intra-continuity by cascade gated recurrent unit (GRU). A discriminant slice grouping subnetwork is then developed to model the correlation between ridges slices and implicitly discover the discriminant inter-continuity. Pruning strategy is further utilized to reduce network parameters and promote its practical application in real scenarios. Experimental results, evaluated on three publicly available datasets, show the competitiveness of our method. Furthermore, in addition to reducing computational complexity, our method also shows the best ACE performance in cross-material and cross-sensor cases.