Deep learning

Pattern recognition (psychology)

Overfitting

Algorithm

Machine learning

Residual

Liveness

Fingerprint recognition

Artificial intelligence

Fingerprint (computing)

Computer science

Theoretical computer science

Computer vision

Artificial neural network

Convolutional neural network

IEEE Access

Fingerprint liveness detection has gradually been regarded as a primary countermeasure for protecting the fingerprint recognition systems from spoof presentation attacks. The convolutional neural networks (CNNs) have shown impressive performance and great potential in advancing the state-of-the-art of fingerprint liveness detection. However, most existing CNNs-based fingerprint liveness methods have a few shortcomings: 1) the CNN structure used on natural images does not achieve good performance on fingerprint liveness detection, which neglects the inevitable differences between natural images and fingerprint images; or 2) a relative shallow architecture (typically several layers) has not paid attention to the capability of deep network for spoof fingerprint detection. Motivated by the compelling classification accuracy and desirable convergence behaviors of the deep residual network, this paper proposes a new CNN-based fingerprint liveness detection framework to discriminate between live fingerprints and fake ones. The proposed framework is a lightweight yet powerful network structure, called Slim-ResCNN, which consists of the stack of series of improved residual blocks. The improved residual blocks are specifically designed for fingerprint liveness detection without overfitting and less processing time. The proposed approach significantly improves the performance of fingerprint liveness detection on LivDet2013 and LivDet2015 datasets. Additionally, the Slim-ResCNN wins the first prize in the Fingerprint Liveness Detection Competition 2017, with an overall accuracy of 95.25%.

Slim-ResCNN: A Deep Residual Convolutional Neural Network for Fingerprint Liveness Detection