基于动态径向基神经网络的人脸识别算法研究

提出了一个通用而且有效的方法来设计RBF神经网络分类器用于人脸识别。为了避免过拟合和减少计算量,用主元分析法和Fisher线性判别技术来降低维数,以提取人脸特征;利用一个混合的学习算法来训练RBF神经网络,使梯度下降法的搜索空间大大减少;采用一种基于训练样本类别信息的新的聚类算法,所有同类的数据可被聚集在一起,尽量减少不同类数据混杂在一起,同时选取结构尽可能紧凑的RBF神经网络分类器。在ORL数据库上进行了仿真,实验结果表明,该算法具有高效性和有效性。     

几种机器学习方法在人脸识别中的性能比较

BP神经网络、RBF神经网络、支持向量机（SVM）和集成学习是目前应用最为广泛的四种机器学习方法。将这四种常用的机器学习方法分别应用于人脸识别,并利用ORL人脸图像库对各学习方法性能进行了测试和评估。测试结果表明SVM和集成学习在实验中取得了较好的性能,最适合用于人脸识别中特征分类器。     

基于卷积特征和贝叶斯分类器的人脸识别

为解决传统人脸识别算法特征提取困难的问题,提出了基于卷积特征和贝叶斯分类器的人脸识别方法,利用卷积神经网络提取人脸特征,通过主成分分析法对特征降维,最后利用贝叶斯分类器进行判别分类,在ORL（olivetti research laboratory）人脸库上进行实验,获得了99.00%的识别准确率。实验结果表明,卷积神经网络提取的人脸图像特征具有很强的辨识度,与PCA（principal component analysis）和贝叶斯分类器结合之后可有效提高人脸识别的准确率。     

基于SVM的人脸检测研究

支持向量机（SVM）是在统计学习理论基础上发展起来的一种新的机器学习算法。依据SVM良好识别和泛化能力,实现了一种基于支持向量机的图像人脸识别方法。利用Opencv提取样本类的低层特征,训练具有径向基核函数的SVM分类器,在VS2008和Qt平台下实现识别软件开发。运行结果表明,软件具有良好的图像人脸检测能力。     

Combined Classifiers for Invariant Face Recognition

This paper presents a system for invariant face recognition. A combined classifier uses the generalisation capabilities of both Learning Vector Quantisation (LVQ) and Radial Basis Function (RBF) neural networks to build a representative model of a face from a variety of training patterns with different poses, details and facial expressions. The combined generalisation error of the classifier is found to be lower than that of each individual classifier. A new face synthesis method is implemented for reducing the false acceptance rate and enhancing the rejection capability of the classifier. The system is capable of recognising a face in less than one second. The well-known ORL database is used for testing the combined classifier. Comparisons with several other systems show that our system compares favourably with the state-of-the-art systems. In the case of the ORL database, a correct recognition rate of 99.5% at 0.5% rejection rate is achieved.     

Research of assembling optimized classification algorithm by neural network based on Ordinary Least Squares (OLS)

A new optimized classification algorithm assembled by neural network based on Ordinary Least Squares (OLS) is established here. While recognizing complex high-dimensional data by neural network, the design of network is a challenge. Besides, single network model can hardly get satisfying recognition accuracy. Firstly, feature dimension reduction is carried on so that the design of network is more convenient. Take Elman neural network algorithm based on PCA as sub-classifier I. The recognition precision of this classifier is relatively high, but the convergence rate is not satisfying. Take RBF neural network algorithm based on factor analysis as sub-classifier II. The convergence rate of the classifier algorithm is fast, but the recognition precision is relatively low. In order to make up for the deficiency, by carrying on ensemble learning of the two sub-classifiers and determining optimal weights of each sub-classifier by OLS principle, assembled optimized classification algorithm is obtained, so to some extent, information loss caused by dimensionality reduction in data is made up. In the end, validation of the model can be tested by case analysis.     

基于岛屿迁徙模型的RBF网络集成及其应用研究

神经网络集成是一种通过组合每个神经网络的输出生成最后预测的很流行的学习方法,可以显著地提高学习系统的泛化能力。为了提高集成方法的有效性,提出了一种基于分而治之的思想和岛屿迁徙模型的径向基神经网络集成的新方法。实验结果表明,岛屿迁徙神经网络集成预测模型不但可以提高系统对多维空间的高维搜索能力,简化网络结构,而且在产品的自动化检测试验中也可获得更高的预测精度。     

Human face recognition based on ensemble of polyharmonic extreme learning machine

This paper proposes a classifier named ensemble of polyharmonic extreme learning machine, whose part weights are randomly assigned, and it is harmonic between the feedforward neural network and polynomial. The proposed classifier provides a method for human face recognition integrating fast discrete curvelet transform (FDCT) with 2-dimension principal component analysis (2DPCA). FDCT is taken to be a feature extractor to obtain facial features, and then these features are dimensionality reduced by 2DPCA to decrease the computational complexity before they are input to the classifier. Comparison experiments of the proposed method with some other state-of-the-art approaches for human face recognition have been carried out on five well-known face databases, and the experimental results show that the proposed method can achieve higher recognition rate.     

基于统计模式的人脸识别方法

人脸识别的研究具有广泛的应用价值。论文对基于统计模式的人脸识别方法进行了深入的研究,提出了使用D-LDA与KDDA算法对特征进行提取。D-LDA算法克服了传统方法计算复杂的缺点;KDDA算法利用了基于核的分析方法,避免了利用线性方法处理问题产生的缺陷。文中重点研究了RBF网络分类器,对于参数的选择以及网络的学习过程采用了改进的算法,克服了常用方法中存在的问题。实验表明采用改进后的算法,识别率得到了提高。     

基于EMRBF情感神经网络的人脸识别

在现有RBF神经网络基础上引入情感因子,提出了一种情感径向基神经网络（EMRBF）,给出了EMRBF的结构,定义了新的训练准则函数,推导出了EMRBF网络权值训练算法,把EMRBF网络用于人脸识别系统。先采用PCA和LDA相结合进行人脸特征提取．然后设计EMRBF人脸分类器。在ORL人脸库上的实验结果表明,EMRBF网络的识别率达到98％,与普通RBF神经网络相比,性能明显提高。