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近年来,人脸识别技术得到了越来越多的应用。为了提高人脸识别的准确率,采用人工神经网络完成人脸图像的识别。在识别过程中,采用主成分分析法(PCA)进行人脸图像特征提取,将BP神经网络用于人脸识别,建立人脸识别模型,并利用ORL人脸数据库进行仿真实验。实验结果表明,该识别方法以及识别模型在实际应用中是可行的。该识别模型简单,识别效率较高,如果能够适当增加隐含层数和改进识别算法,可提高人脸识别系统的识别率和实时性。
In recent years, face recognition technology has been more and more applications. In order to improve the accuracy of face recognition, artificial neural network is used to recognize face images. In the recognition process, the principal component analysis (PCA) is used to extract face image features, BP neural network is used for face recognition, the face recognition model is established, and the ORL face database is used for simulation. Experimental results show that the identification method and identification model are feasible in practical application. The recognition model is simple and has high recognition efficiency. If the number of hidden layers and the recognition algorithm can be increased properly, the recognition rate and real-time performance of the face recognition system can be improved.