基于人脸识别技术的公共图书馆用户行为自动监控方法

传统的用户行为监控方法判断误差率较大，为此，提出以人脸识别技术为基础，对公共图书馆用户行为自动监控的方法，通过计算分析图像多维空间数据，得到人脸识别所需要的核函数与密度梯度，对比面部帧和背景模型，得到面部矩形区域划分结果，凭借特征计算构建弱分类器，将多组弱分类器组合构成强分类器，再将几组强分类器组合成级联分类器，同时通过精确定位算法对用户图像内眼睛与嘴巴位置定位，完成对公共图书馆用户行为的监控。实验结果证明，所提方法能够提升行为监测结果的精准度。     

基于DCT和神经网络的人脸识别

人脸识别是模式识别领域的一个具有挑战性的课题，并且有着潜在的应用前景。该文提出了基于DCT和神经网络的人脸识别方法，针对人脸图像分别提取整体和局部的DCT系数共同送入多层感知机分类器分类，实验表明所提出的方法具有识别速度快、识别率较高的综合优势。     

纹理特征和两级分类器相结合的人脸识别

为了提高人脸识别率和识别效率,提出一种纹理特征和两级分类器相结合的人脸识别方法。采用灰度共生矩阵表示人脸图像的纹理特征,计算待识别人脸图像与模板间欧式距离,采用拒识阈值进行评判,如果人脸图像归属类别清楚,则采用欧式距离分类器进行识别,否则将待识人脸图像送入SVM分类器进行识别,采用ORL人脸数据库和Yale人脸数据库进行仿真实验。仿真结果表明,相对于单一人脸识别器,两级分类器不仅提高了人脸识别效率,而且提高了人脸识别率,具有更好的人脸识别性能。     

基于线性投影分析和FCM动态聚类的人脸识别*

依据主成分分析方法(PCA)对图像具有很好的表达能力,即能很好地重构原图像,而线性鉴别分析(LDA)可使图像样本具有较高可分性的特点,提出对图像先进行PCA处理,再进行LDA处理,从而降低人脸特征维数并对人脸图像进行了特征提取;并提出用FCM动态聚类算法作为识别分类器,对人脸进行识别。实验和分析结果表明,在人脸识别中,这种融合PCA和LDA的分类方法能够更好地对特征进行提取,且FCM动态聚类分类器比K近邻判别分类器更具有灵活的分类能力。     

复杂光照条件下的人脸预处理算法

为了提高光照条件下的人脸识别正确率,提出一种复杂光照条件下的人脸预处理算法。对人脸图像进行局部增强处理,用双边滤波对图像亮度进行估计,采用Gamma校正补偿图像亮度估计产生的损失,将反射分量与亮度估计结果融合获得效果更优的人脸图像,并用K近邻算法建立分类器对人脸进行识别。在Yale、PIE和AR人脸库仿真结果表明,该算法提高了光照条件下的人脸识别正确率,其性能优于当前典型人脸识别算法。     

基于多阈值局部二值模式的人脸识别方法

提出一种基于多阈值局部二值模式(MTLBP)的人脸识别方法。计算图像中每个像素点与其局部邻域点的灰度差,通过选择不同的阈值编码形成MTLBP,采用多区域直方图向量进行人脸特征描述,模糊化多阈值匹配结果进行人脸识别。实验结果表明,该方法能很好地结合人脸的纹理和梯度信息,对表情等变化具有较好的鲁棒性。     

通过创建虚拟样本的小样本人脸识别统计学习方法

研究了人脸识别方法．在传统的“特征脸”方法基础上，提出了一种基于贝叶斯方法的小样本人脸识别方法，该方法对于经过预处理的标准人脸图像，通过原型脸创建虚拟样本，扩充样本数量，然后用PCA降维并提取人脸图像的特征．对提取的特征用BEM算法学习该类样本的概率密度分布参数，构建贝叶斯混合网络分类器．该方法可以有效地解决统计学习方法中样本数量不足问题，提高小样本人脸识别方法的识别率，同样可以运用于模式识别中其它对象识别．实验表明，该方法能提高小样本人脸识别率，有实际应用价值。     

基于小波变换和多分类器的人脸识别方法

现在已有许多特征用于人脸识别，而不同的特征反映了图像的不同特性。因此，结合多个特征，使用多分类器来进行分类可以提高识别率。文中在对原始图像进行小波变换预处理的基础上，抽取本征脸特性的奇异值特征，并利用对应着两类特征的多分类器进行分类。利用ORL人脸库进行了实验，实验结果证明了所提方法的有效性。     

融合双向2DLDA和局部SVD的人脸识别

针对人脸识别中光照、表情、姿态的影响,提出一种融合双向二维线性鉴别分析和局部对称平均的人脸识别新方法。通过双向二维线性鉴别分析对整幅图像进行特征提取,利用局部奇异值分解对称平均提取图像的局部特征。对2种方法提取到的特征利用基于加权欧式距离的最近邻分类器进行融合识别,在ORL人脸库上的实验结果证明了该方法的有效性。     

基于主动表观模型姿态矫正和局部加权匹配人脸识别

非约束环境下,光照、姿态、表情、遮挡等复杂背景因素给人脸识别带来严重影响。提出一种基于AAM（active appearance model）的图像对齐和局部匹配人脸识别算法,使之能够增强人脸识别算法对姿态、表情变化的鲁棒性。AAM能够快速准确地定位人脸的特征点,进而将图像扭转到一个标准正面人脸模型中。接着,提出一种新的基于信息熵的Gabor jet加权方法用于提高人脸识别率;并且对Borda count分类器组合方法进行了改进,认为在投票过程中为其设置阈值来排除“噪声”的干扰可以提高识别率。通过与多种人脸识别方法的实验结果比较表明,使用AAM矫正图像后,联合熵加权Gabor方法和加阈值Borda能够取得比单独使用更好的成绩。     

基于Sugeno模糊积分神经网络分类器融合方法在手写数字识别中的应用

神经网络是模式识别中一种常见的分类器.针对同一个分类问题,构建多个分类器并把多个分类器进行融合可以提高分类系统的分类正确率、改善系统的稳健性.首先介绍了Sugeno模糊积分及Sugeno模糊积分神经网络分类器融合方法的一般原理,而后将其应用于手写数字识别,通过实际的案例验证了该融合方法的有效性和可行性.     

Similarity learning for texture image retrieval using multiple classifier system

Multiple Classifier System has found its applications in many areas such as handwriting recognition, speaker recognition, medical diagnosis, fingerprint recognition, personal identification and others. However, there have been rare attempts to develop content-based image retrieval (CBIR) system that uses multiple classifiers to learn visual similarity. Texture as a primitive visual content is often used in many important applications (viz. Medical image analysis and medical CBIR system). In this paper, a texture image retrieval system is developed that learns the visual similarity in terms of class membership using multiple classifiers. The way proposed approach combines the decisions of multiple classifiers to obtain final class memberships of query for each of the output classes is also a novel concept. A modified distance that is weighted with the membership values obtained through similarity learning is used for ranking. Three different algorithms are proposed for the retrieval of images against a query image displaying the strength of multiple classifier approach, class membership score and their interplay to achieve the objective defined in terms of simplicity, retrieval effectiveness and speed. The proposed methods based on multiple classifiers achieve higher retrieval accuracy with lower standard deviation compared to all the competing methods irrespective of the texture database and feature set used. The multiple classifier retrieval schemes proposed here is tested for texture image retrieval. However, these can be used for any other challenging retrieval problems.     

Arabic Word Recognition by Classifiers and Context

Given the number and variety of methods used for handwriting recognition, it has been shown that there is no single method that can be called the "best". In recent years, the combination of different classifiers and the use of contextual information have become major areas of interest in improving recognition results. This paper addresses a case study on the combination of multiple classifiers and the integration of syntactic level information for the recognition of handwritten Arabic literal amounts. To the best of our knowledge, this is the first time either of these methods has been applied to Arabic word recognition. Using three individual classifiers with high level global features, we performed word recognition experiments. A parallel combination method was tested for all possible configuration cases of the three chosen classifiers. A syntactic analyzer makes a final decision on the candidate words generated by the best configuration scheme. The effectiveness of contextual knowledge integration in our application is confirmed by the obtained results.     

改进的贝叶斯多分类器组合规则

多分类器组合是提高识别效果的一条有效途径。文中提出一种用于多分类器组合的改进贝叶斯规则,即首先通过对大量样本的统计获得有关每个分类器识别性能的先验知识,将其作为多分类器组合的依据。组合时对每个类设置不同的阈值,使组合效果得以改善,这些阈值可以通过训练获得。在数字识别中的应用结果表明,改进的贝叶斯规则可以使多分类器的组合结果识别率和置信度得到明显提高。     

Hybrid-boost learning for multi-pose face detection and facial expression recognition

This paper proposes a hybrid-boost learning algorithm for multi-pose face detection and facial expression recognition. To speed-up the detection process, the system searches the entire frame for the potential face regions by using skin color detection and segmentation. Then it scans the skin color segments of the image and applies the weak classifiers along with the strong classifier for face detection and expression classification. This system detects human face in different scales, various poses, different expressions, partial-occlusion, and defocus. Our major contribution is proposing the weak hybrid classifiers selection based on the Harr-like (local) features and Gabor (global) features. The multi-pose face detection algorithm can also be modified for facial expression recognition. The experimental results show that our face detection system and facial expression recognition system have better performance than the other classifiers.     

Combination of multiple classifiers using probabilistic dictionary and its application to postcode recognition

Combination of multiple classifiers is regarded as an effective strategy for achieving a practical system of handwritten character recognition. A great deal of research on the methods of combining multiple classifiers has been reported to improve the recognition performance of single characters. However, in a practical application, the recognition performance of a group of characters (such as a postcode or a word) is more significant and more crucial. With the motivation of optimizing the recognition performance of postcode rather than that of single characters, this paper presents an approach to combine multiple classifiers in such a way that the combination decision is carried out at the postcode level rather than at the single character level, in which a probabilistic postcode dictionary is utilized as well to improve the postcode recognition ability. It can be seen from the experimental results that the proposed approach markedly improves the postcode recognition performance and outperforms the commonly used methods of combining multiple classifiers at the single character level. Furthermore, the sorting performance of some particular bins with respect to the postcodes with low frequency of occurrence can be improved significantly at the same time.     

基于Adaboost算法的人眼状态检测

人眼检测在表情识别和人脸识别中起着非常重要的作用,作为一种预处理的手段,人眼检测和定位可以有效地提高表情识别和人脸识别的识别率.提出了一种基于Adaboost算法的实时人眼状态检测的方法.Adaboost是一个构造准确分类器的学习方法.它将一簇弱分类器通过一定的规则结合成为一个强分类器,再把这些强分类器级联成为一个快速、准确的分类器.分析和讨论训练阶段不同的人眼特征选择对最终检测的影响,并实验测试各种特征方法对特定目标的检测率,给出一个理想的分类器.     

多分类器融合在银行票据识别中的应用

论文将数字识别技术应用于银行票据自动识别勾兑业务系统,并利用多分类器融合方法获得了99.527%的单个数字字符识别率,在此基础上运用多区域多内容冗余校验方法保证了识别结果的充分可信赖性,经此处理后整张票据的识别通过率高达87.573%,其识别结果完全正确。     

一种基于几何关系的多分类器差异性度量及其在多分类器系统构造中的应用

多分类器系统是应对复杂模式识别问题的有效手段之一. 当子分类器之间存在差异性或互补性时,多分类器系统往往能够获得比单分类器更高的分类正确率. 因而差异性度量在多分类器系统设计中至关重要. 目前已有的差异性度量方法虽能够在一定程度上刻画分类器之间的差异,但在应用中可能出现诸如差异性淹没等问题. 本文提出了一种基于几何关系的多分类器差异性度量,并在此基础上提出了一种多分类器系统构造方法,同时通过实验对比了使用新差异性度量方法和传统方法对多分类器系统融合分类正确率的影响. 结果表明,本文所提出的差异性度量能够很好地刻画分类器之间的差异,能从很大程度上抑制差异性淹没问题,并能有效应用于多分类器系统构造.     

基于Adaboost的架空输电线路巡线机器人障碍识别

巡线机器人沿电力线行走时必须探测和识别各种障碍物,同时根据障碍类型规划越障行为。文章提出了一种基于Adaboost算法的架空输电线路巡线机器人障碍物识别方法。Adaboost是一个构造准确分类器的学习方法,它把一簇弱分类器通过一定的规则结合成为一个强分类器,再把这些强分类器级联成为一个快速、准确的分类器。实验结果证明了该方法的可行性。