摘要
为了提高人脸识算法的训练识别速度以及准确率,提出一种改进的人脸识别算法。将独立成分分析(independent component analysis,ICA)与径向基函数(radial basis function,RBF)神经网络相结合,利用ICA算法对人脸图像进行特征提取,采用牛顿迭代法提升其迭代性能;引入松弛因子,在保证收敛速度的前提下,放宽对初始权值选取的局限性。将特征信息作为RBF神经网络学习输入,采用监督聚类方法对神经网络进行构建和初始化,利用线性最小二乘法调整输出层连接权值,梯度下降法调整隐含层中心以及高斯宽带,通过训练学习获得最终的人脸识别分类结果。对比实验结果表明,改进的人脸识别算法训练速度和识别速度更快,准确率更高。
In order to improve the recognition speed and the accuracy of face recognition algorithm,an improved face recognition algorithm based on independent component analysis(ICA)and radial basis function(RBF)neural network is proposed in this paper.Firstly,ICA algorithm is used to extract features from face images,and the extended Newton iteration method is used to improve the iterative performance.The relaxation factor is introduced to relax the limitation of initial weight selection on the premise of ensuring the convergence speed.Then the feature information is used as the input for RBF neural network learning.The neural network is therefore constructed and initialized by using supervised clustering method.The linear least squares method is used to adjust the connection weights of the output layer,and the gradient descent method is for the hidden layer centre and Gaussian broadband.The final face recognition classification results is obtained through training.Experimental results show that the improved face recognition algorithm in this paper has faster training and higher recognition speeds,and therefore more accuracy on face recognition.
引文
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