摘要
介绍一种基于PCA和神经网络结合的人脸识别方法。该方法首先利用主成分分析方法对整幅图像进行特征提取,获得最佳描述特征,从而减小神经网络的输入。然后将降维之后的图像数据输入到一个前向传播神经网络中训练。神经网络的权值采用粒子群算法进行优化,用标准人脸数据库中的样本进行测试,最后将该方法与其他方法作了比较。实验结果表明,该方法能够取得更好的效果。
A new approach for face recognition based on Principal Component Analysis （PCA） and neural networks is proposed. First the PCA is used to obtain the best description features over the entire image. It can reduce the input of the neural networks. Then the reduced image data is delivered to a forward neural network for training. The weights of the network is optimized by using the particle swarm optimization （PSO）. This method was tested by using the standard ORL face database. The results show that the method is more effective than the others.
出处
《山东科学》
CAS
2006年第4期63-67,共5页
Shandong Science
关键词
主成分分析
神经网络
粒子群算法
人脸识别
principal component analysis（PCA）
neural networks
particle swarm optimization （PSO）
face recognition
