A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection  

作　　者：Jie Chen Chengsheng Yuan Chen Cui Zhihua Xia Xingming Sun Thangarajah Akilan 

机构地区：[1]School of Computer Science,Nanjing University of Information Science and Technology,Nanjing,210044,China [2]Key Laboratory of Public Security Information Application Based on Big-Data Architecture,Ministry of Public Security,Zhejiang Police College,Hangzhou,310053,China [3]Jiangsu Yuchi Blockchain Research Institute,Nanjing,210044,China [4]Department of Software Engineering,Lakehead University,Thunder Bay,ON P7B 5E1,Canada

出　　处：《Computers, Materials & Continua》2022年第10期719-733,共15页计算机、材料和连续体（英文）

基　　金：This work is supported by the National Natural Science Foundation of China under grant,62102189,U1936118,U1836208,U1836110,62122032;by the Jiangsu Basic Research Programs-Natural Science Foundation under grant BK20200807;by the Key Laboratory of Public Security Information Application Based on Big-Data Architecture,Ministry of Public Security(2021DSJSYS006);by the Research Startup Foundation of NUIST 2020r15.

摘　　要：Fingerprint identification systems have been widely deployed in many occasions of our daily life.However,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit fingerprints.To address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s attention.The vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model generalization.Aiming at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint detection.Next,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection model.Finally,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and cross-sensor.The size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available.

关 键 词：FLD LIGHTWEIGHT MULTI-SCALE RSC blind materials 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]