基于卷积神经网络的人脸识别算法

Face Recognition Algorithm Based on CNN

传统的人脸识别技术对人脸图像特征的提取及分类器选择均较为复杂,且识别率也不高,随着卷积神经网络从手写数字识别到人脸识别的技术不断成熟,提出了一种利用Python+Keras框架测试CNN的人脸识别算法。该方法主要涉及两方面,一是通过改变隐藏层神经元数量查看对网络的影响;另一个是通过改变卷积层1和卷积层2特征图数量查看对网络的影响。通过多组实验测试得到最佳的CNN模型为36-76-1024,该模型可以自动提取人脸图像特征并分类,使用adam优化器和softmax分类器进行人脸识别可以让训练更快收敛和更有效提高准确率,并利用Dropout方法避免过拟合。实验结果表明,CNN模型在olivettifaces人脸库上的识别率达到了97.5%,当采用最佳CNN模型时平均识别率接近100%,从而验证了该算法及模型的有效性及准确性。

The traditional face recognition technology is more complicated for the extraction of facial image features and the selection of classifiers,and the recognition rate is not high.With the continuous maturity of the convolutional neural network from handwritten digit recognition to face recognition.A face recognition algorithm that tests CNN using the Python+Keras framework.The method mainly involves two aspects.One is to observe the influence on the network by changing the number of neurons in the hidden layer,the other is to observe the influence on the network by changing the number of feature maps of the convolutional layer 1 and the convolutional layer 2.The best CNN model is 36-76-1024 through multiple sets of experimental tests.The model can automatically extract facial image features and classify them.Using adam optimizer and softmax classifier for face recognition can make training faster convergence and more.Effectively improve accuracy and use the Dropout method to avoid overfitting.The experimental results show that the recognition rate of the CNN model on the olivettifaces face database is 97.5%.When the optimal CNN model is used,the average recognition rate is close to 100%,which verifies the validity and accuracy of the algorithm and model.