Analysing the Performance of LSTMs and CNNs on 1310 nm Laser Data for Fingerprint Presentation Attack Detection

Abstract

Due to the wide operational deployment of biometric recognition systems, presentation attacks targeting the capture device have become a severe threat. Especially for fingerprint recognition, a high number of different materials allows the creation of numerous presentation attack instruments (PAIs) in the form of full fake fingers and fingerprint overlays, which very much resemble the skin properties at fingertips. As a consequence, automated presentation attack detection (PAD) mechanisms are of utmost importance. Utilising a 1310 nm laser in a new capture device, we present an evaluation of three long short-term memory (LSTM) networks in comparison to eight convolutional neural networks (CNNs) on a database comprising over 22,000 samples and including 45 different PAI species. The LSTMs analyse temporal properties within a captured sequence in order to detect blood movement, while the CNNs take into account spatial properties within a single frame to focus on reflections by the PAI material. The results show that the diversity of PAI species is too big for a single classifier to correctly detect all presentation attacks. However, by fusing the scores from distinct algorithms, we can achieve a detection accuracy of 3.71% APCER for a convenient BPCER of 0.2%.