计算机应用与软件Issue(1)：158-161,4.DOI:10.3969/j.issn.1000-386x.2014.01.041
一种基于改进BP神经网络的PCA人脸识别算法
PCA FACE RECOGNITION ALGORITHM BASED ON IMPROVED BP NEURAL NETWORK
摘要
Abstract
Face recognition,as a focus of the research in pattern recognition field,has gained increasing attention.Traditional BP algorithm has a strong ability in self-learning,self-adaptivity and nonlinear mapping.Moreover,it has a significant predominance in human face recognition accuracy.However,the algorithm also has shortages including slow convergence,training process oscillation and easy to fall into local minima.In light of these deficiencies of traditional BP neural network,we propose a PCA face recognition algorithm which is based on improved BP neural network.The algorithm uses PCA algorithm to extract principal features of face image and uses a new weight adjustment method to improve the BP algorithm for image classification and recognition.Simulation experimental results show that faster convergence speed and higher recognition rate are achieved when using the improved algorithm to identify the images in ORL face database than the traditional algorithm.关键词
人脸识别/主成分分析/BP神经网络/附加动量/弹性梯度下降法Key words
Face recognition/Principal component analysis/BP neural network/Additional momentum/Elastic gradient descent method分类
信息技术与安全科学引用本文复制引用
李康顺,李凯,张文生..一种基于改进BP神经网络的PCA人脸识别算法[J].计算机应用与软件,2014,(1):158-161,4.基金项目
国家自然科学基金项目（70971043）；江西省教育厅科学技术研究项目（GJJ112348）。 （）
