FLDNet: Light Dense CNN for Fingerprint Liveness Detection

Fingerprint liveness detection has gained increased attention recently due to the growing threat of spoof presentation attacks. Among the numerous attempts to deal with this problem, the Convolutional Neural Networks (CNN) based methods have shown impressive performance and great potential. However, there is a need for improving the generalization ability and reducing the complexity. Therefore, we propose a lightweight (0.48M parameters) and efficient network architecture, named FLDNet, with an attention pooling layer which overcomes the weakness of Global Average Pooling (GAP) in fingerprint anti-spoofing tasks. FLDNet consists of modified dense blocks which incorporate the residual path. The designed block architecture is compact and effectively boosts the detection accuracy. Experimental results on two datasets, LivDet 2013 and 2015, show the proposed approach achieves state-of-the-art performance in intra-sensor, cross-material and cross-sensor testing scenarios. For example, on LivDet 2015 dataset, FLDNet achieves 1.76% Average Classification Error (ACE) over all sensors and 3.31% against unkown spoof materials compared to 2.82% and 5.45% achieved by state-of-the-art methods.