基于卷积神经网络的智慧校园人脸识别系统设计与研究

Design and research of convolutional neural networks based intelligent campus face recognition system

为了让学生的学习、生活更加智能化,提高教学管理效率,同时建立一个更加安全的校园环境,采用卷积神经网络实现智慧校园人脸识别。文章对卷积神经网络的卷积层、池化层、全连接层和输出层的原理及实现进行了阐述,训练了olivettifaces人脸数据库小样本数据集。实验结果表明,模型的误差率降低到5%以下。用数据库中注册的人脸图像与摄像头实时获取的人脸图像进行匹配时,效果良好,能满足设计需求。

In order to make students'study and life more intelligent,improve the efficiency of teaching management,and at the same time establish a safer campus environment,convolutional neural networks are used to realize smart campus face recognition.This paper describes the principles and implementation of the convolutional layer,pooling layer,fully connected layer and output layer of the convolutional neural networks.The small sample data set of the olivettifaces face database is trained.The experiment results show that the error rate of the model is reduced to 5%or less.When the face image registered in the database is used to match the face image obtained by the camera in actual time,the effect is good and can meet the design requirements.