带有特征信息卷积神经网络的人脸识别算法

Face Recognition Algorithm Based on Convolutional Neural Network with Feature Information

图像分类中,卷积神经网络在人脸识别中取得了较大的进展。在卷积提取人脸图像特征信息操作时,当卷积核数目有限的情况下,可能提取到的特征值,如头发、纹理等,并不能很好的代表该人的主要特征,从而导致识别率降低,而增加卷积核数目又会导致识别时间增加。针对这一问题,提出了一种基于特征信息卷积神经网络的人脸识别方法。该方法在图像处理过程中,使用奇异值分解,选取前4个奇异值代表人脸的主要特征,快速滤除大部分无用的特征信息,形成新的图像特征模板库。利用卷积网络在提高网络感受野的同时不丢失特征图信息的优势,融合最具有代表性的特征信息,最大程度地捕捉图像信息。采用卷积神经网络模型和基于奇异值分解的特征融合的结构模型实现人脸识别,仿真实验结果表明,这种方法减少了算法的训练时间,提高了人脸识别的准确性。

In image classi cation,convolution neural network has made great progress in face recognition.When convolution is used to extract face image feature information,when the number of convolution kernels is limited,the feature values,such as hair,texture,may not represent the main features of the person well,resulting in the reduction of recognition rate.To solve this problem,a face recognition method based on feature information convolution neural network is proposed in this paper.In the process of image processing,ingular value decomposition is used to select the rst four singular values to represent the main features of the face,and most of the useless feature information is quickly ltered out.The convolution network can improve the receptive eld of the network without losing the information of the feature map,and fuse the most representative feature information.The convolutional neural network model and the structural model of feature fusion based on singular value decomposition are used to realize face recognition.The simulation results show that this method reduces the training time of the algorithm and improves the accuracy of face recognition.