Fingerprint Liveness Detection by a Template-Probe Convolutional Neural Network

IEEE Access

Ho Yub Jung¹  Yong Seok Heo²  Soochahn Lee³ 

[1] Department of Computer Engineering, Chosun University, Gwangju, South Korea;Department of Electrical and Computer Engineering, Ajou University, Suwon, South Korea;School of Electrical Engineering, Kookmin University, Seoul, South Korea;

关键词: Convolutional neural network;    fingerprints;    LivDet;    liveness detection;    pretraining;    transfer learning;   

DOI  :  10.1109/ACCESS.2019.2936890

来源: DOAJ

【 摘 要 】

Fingerprints are known to be easily synthesized to trick identification systems. In this paper, we propose a new method that incorporates template fingerprints stored for identification in the liveness detection system. The fingerprint identification platform must have a list of template fingerprints stored for matching with new probe fingerprints trying to access the system. Thus, instead of simply detecting the liveness of the probe fingerprints, the proposed approach uses the matching template fingerprints along with probe fingerprints through convolutional neural networks to make the liveness decision, which comprises two sequential convolutional neural networks for classification. The proposed method can be built on the top of existing liveness detection methods to increase accuracy without a significant increase in computation time. The evaluation over the LivDet dataset shows that the proposed fingerprint liveness detection method is able to obtain state-of-the-art accuracy.

【 授权许可】

Unknown