采用轻量化神经网络的高安全手指静脉识别系统

High-Security Finger Vein Recognition System Using Lightweight Neural Network

针对特殊材料能伪造手指静脉从而欺骗识别系统,以及利用卷积神经网络进行手指静脉识别计算量大的问题,设计了具有活体检测功能和轻量化卷积神经网络结构的手指静脉识别系统。采用光容积法检测手指脉搏波的变化,从而判断被采集对象是否为活体;利用剪枝及通道恢复方法改进了ResNet-18卷积神经网络,并结合L_(1)正则化增加卷积神经网络的特征选择能力,在提升算法准确率的基础上,能有效地降低计算资源的消耗。实验表明,使用改进的剪枝及通道恢复优化结构,参数量降低了75.6%,计算量降低了25.6%,在山东大学和香港理工大学手指静脉数据库上得到的等误率分别为0.025%、0.085%,远低于ResNet-18得到的等误率(0.117%、0.213%)。

Special materials can fake finger veins to deceive recognition systems,and the large amount of computation is required for finger vein recognition using convolutional neural networks.In this study,a finger vein recognition system with an in vivo detection function and a lightweight convolutional neural network structure was designed.Changes in finger pulse waves were detected using Photo Plethysmo Graphy to determine whether the object was living.The ResNet-18 convolutional neural network was improved using a pruning and channel recovery method,and L_(1) regularization was combined to improve the feature selection ability of the convolutional neural network.On the basis of improving the accuracy of the algorithm,the network can effectively reduce the consumption of computing resources.The experimental results show that by using the improved pruning and channel recovery optimization structure,the number of parameters decreases by 75.6%,the amount of calculations decreases by 25.6%,and the iso-error rates obtained in the Shandong University and Hong Kong Polytechnic University finger vein databases are 0.025%and 0.085%,respectively,which are significantly lower than those of RESNET-18(0.117%and 0.213%).