BP神经网络算法在人脸识别中的应用研究

Application of BP Neural Network Algorithm in Face Recognition

人工智能作为划时代的思维技术,正在悄然改变着这个世界。机器学习(maching learning)作为人工智能重要部分之一,一直受到社会的普遍关注,机器学习中的人脸识别更是一大热门领域。其中,神经网络作为人脸识别中一种重要的组成部分,在科研和实际应用中的作用毋庸置疑。本文学习运用反向传播(Backpropagation)算法结合梯度下降法对人工神经网络进行训练,通过PCA(主成分分析法)对图片进行处理,将得到的特征值作为BP神经网络的输入,从而实现了人脸朝向的分类,人脸表情的分类,人脸的匹配识别。具体由Python实现神经网络的建立,用Sklearn库实现PCA方法对图片进行处理,数据集使用耶鲁大学人脸表情数据库。

As an epoch-making thinking technology,artificial intelligence is quietly changing the world.As one of the important parts of artificial intelligence,machine learning has received widespread attention from the society.Face recognition in machine learning is a hot area.Among them,neural network as an important part of face recognition,the role in scientific research and practical application is beyond doubt.In this paper,we use the backpropagation algorithm combined with the gradient descent method to train the artificial neural network.The PCA(principal component analysis)is used to process the image,and the obtained eigenvalue is used as the input of the BP neural network.The classification of face orientation,the classification of face expressions,and the matching recognition of faces.Specifically,the establishment of a neural network is implemented by Python,and the PCA method is used to implement the image processing using the sklearn library.The data set uses the Yale University face expression database.