摘要
在不同证件审核场景中,由于存在年龄跨度、装扮及样本缺乏等因素的影响,使得现有方法难以适应不同证件照下的人脸识别,无法满足实际应用要求。为解决上述问题,提出一种基于深度卷积神经网络的不同证件照识别方法。该方法对VGG网络做出适应于不同证件照识别的改进,实现端到端的自主学习人脸特征,消除年龄跨度、装扮等因素的影响,并且可将训练参数减少为原网络结构的1/6,使得在保证识别精度的同时,模型训练时间大幅减小。实验结果表明,该方法在高校毕业审核场景下的自建数据集和CAS-PEAL-R1公开数据集上训练后,验证准确率和召回率较原始方法分别提高了6.29个百分点和7个百分点,能够满足多种应用场景下的不同证件审核需求。
In different authentication scenarios,it is difficult to adapt the existing methods to face recognition under different authentication photos for the sake of the influence of age span,dress-up and lack of samples,which cannot conform to the practical application requirements.For the sake of solving the above problems,it puts forward a different identification method on the basis of the deep convolution neural network.This method makes the improvement of VGG network adapted to different document photo recognition,realizing end-to-end autonomous learning of face features,eliminating the influence of age span,dress-up and other factors.In addition,the method cuts down the trainable parameters to 1/6 of the original network structure,thus ensuring the identification accuracy while greatly reducing the training time of the model.According to the experimental results,after training on the self-built data set and CAS-PEAL-R1 public data set under the college graduation examination scene,the verification accuracy and recall rate of this method were 6.29 and 7 percentage points higher than the original method respectively,which can conform to the different document examination needs under various application scenarios.
作者
李硕
卞青山
刘传文
刘鸣涛
张林涛
LI Shuo;BIAN Qing-shan;LIU Chuan-wen;LIU Ming-tao;ZHANG Lin-tao(School of Information Science and Engineering,Linyi University,Linyi 276000,China;Academic Affair Office,Linyi University,Linyi 276000,China)
出处
《计算机与现代化》
2020年第2期104-109,共6页
Computer and Modernization
基金
临沂大学博士启动研究基金资助项目(LYDX2016BS115,LYDX2016BS114)
关键词
人脸识别
证件审核
卷积神经网络
人脸验证
face recognition
ID photo verification
convolutional neural network
face verification
