小样本条件下采用Gabor特征的人脸识别

人脸表征和特征提取是人脸识别中的关键问题．针对Gabot特征的识别能力问题,利用点分布模型和类别可分离性判据研究了人脸不同位置和不同Gabor展开系数的分类能力．实验结果表明,合理地构造Gabor特征和选择特征点位置,能够提高识别率和减少特征数量．在此基础上,提出了在小样本条件下结合主动形状模型和Gabor特征进行人脸识别的方法．     

A combination of Genetic Algorithm,Particle Swarm Optimization and Neural Network for palmprint recognition

In this study, a new approach to the palmprint recognition phase is presented. 2D Gabor filters are used for feature extraction of palmprints. After Gabor filtering, standard deviations are computed in order to generate the palmprint feature vector. Genetic Algorithm-based feature selection is used to select the best feature subset from the palmprint feature set. An Artificial Neural Network (ANN) based on hybrid algorithm combining Particle Swarm Optimization (PSO) algorithm with back-propagation algorithms has been applied to the selected feature vectors for recognition of the persons. Network architecture and connection weights of ANN are evolved by a PSO method, and then, the appropriate network architecture and connection weights are fed into ANN. Recognition rate equal to 96% is obtained by using conjugate gradient descent algorithm.

     

基于Adaboost的人脸与虹膜融合识别

传统生物特征识别系统的识别率经常受到环境以及生物学特征的自身局限性影响。针对该不足,提出一种基于人脸与虹膜特征级融合的多模态生物识别系统,采用中心对称局部二值模式算子提取人脸和虹膜的纹理特征,将人脸特征与虹膜特征线性整合成混合特征向量,利用Adaboost算法从该混合特征向量中优选出一组最佳特征组合,从而构成强分类器。实验结果表明,该多模态系统相比单模态系统具有更好的鲁棒性。     

基于C＋＋和OpenCV的人脸识别系统的设计与实现

人脸识别是通过对人的脸部特征信息进行身份识别的一种生物识别技术.研究现实基于人脸识别的身份验证系统具有重要意义.提出一种基于C＋＋和OpenCV的人脸识别系统的设计与实现方法.系统实现的功能模块包括人脸图像采集、图像预处理、人脸检测与定位、人脸特征提取与识别、人脸数据库管理等.系统中人脸检测采用基于Haar-like特征的Haar 分类器,人脸识别采用Eigenfacae算法.系统经过运行测试,结果表明该系统能够满足一般的基于人脸识别的身份验证需求.     

人脸特征自适应选取技术

如何选取并提取稳定可靠的人脸特征是人脸识别技术中迫切需要解决的问题，文中在对现有特征提取方法的优缺点进行详细分析的基础上，提出人脸特征自适应选取算法框架，详细论述了如何建立并提取人脸特征自适应选取的准则，该算法在100多幅人脸图像实验中效果理想。     

混合卷积神经网络的人脸验证

人脸验证对于个人身份认证很重要, 它在系统安全和犯罪识别中具有重要意义. 人脸验证的任务是给定一对人脸图像判断是否为相同的身份(即二进制分类). 传统的验证方法包括两个步骤: 特征提取和人脸验证. 本文提出了一个混合卷积神经网络, 用于进行人脸验证, 主要过程分为三个步骤: 特征提取, 特征选择和人脸验证. 这个模型关键点是直接使用混合卷积神经网络从原始像素直接学习相关的视觉特征, 并通过单变量特征选择和主成分分析(PCA)进一步处理特征. 这样可以实现从原始像素提取到具有较好鲁棒性和表达性的特征. 在顶层使用支持向量机(SVM)判读是否为同一个人. 通过实验可以发现混合卷积神经网络模型与传统方法相比在人脸验证得准确率上有着较好的表现.     

融合LTP与Fisherfaces的分块人脸识别

提出一种融合局部三值模式(LTP)和Fisherfaces进行人脸识别的方法,运用LTP算子提取分块人脸灰度图像的LTP直方图序列,采用Fisherfaces方法对采样后的特征进行特征选择,根据最近邻原则进行识别。该算法不仅能提取人脸纹理信息,大幅降低训练数据量,而且数据量的维数与原始图像大小无关。在ORL和YALE标准人脸数据库上的实验结果表明,该方法具有较高的识别率。     

基于压缩金字塔核稀疏表示的人脸识别

人脸识别中光照、伪装及姿态等变化一直是富有挑战性的问题,其中特征提取是很关键的一步。为提高人脸识别率,结合压缩感知和空间金字塔模型,本文提出了一种新的特征提取方法,首先用尺度不变特征变换算法提取图像特征,然后与随机生成的字典进行稀疏编码,再用金字塔模型分层提取不同尺度空间的特征,并用最大池融合特征,最后运用核稀疏表示分类。在Extended Yale B,AR 和CMU PIE人脸数据库上的实验结果表明,该方法对于人脸图像的光照、伪装及姿态等变化有较强的鲁棒性,而且该算法有较快的运行速度。     

An SVM-AdaBoost facial expression recognition system

This study is focused on improving the recognition rate and processing time of facial recognition systems. First, the skin is detected by pixel based methods to reduce the searching space for maximum rejection classifier (MRC) which detects the face. The detected face is normalized by a discrete cosine transform (DCT) and down-sampled by Bessel transform. Gabor feature extraction techniques were utilized to extract thousands of facial features that represent facial deformation patterns. An AdaBoost-based hypothesis is formulated to select a few hundreds of Gabor features which are potential candidates for expression recognition. The selected features were fed into a saturated vector machine (SVM) classifier to train it. An average recognition rate of 97.57 % and 92.33 % are registered in JAFFE and Yale databases respectively. The execution time of the proposed method is also significantly lower than others. Generally, the proposed method exhibits superior performance than other methods.     

Fovea intensity comparison code for person identification and verification

In this paper a method is proposed for person identification and verification. The method proposed in Viola and Jones (2001) is used to detect the face region in the image. The detected face region is processed to determine the locations of the eyes and mouth. The facial and mouth features are extracted relative to the locations of the eyes and mouth. A new feature called fovea intensity comparison code (FICC) is obtained from intensity values of the face/mouth region. The dimension of the FICC is reduced using principal component analysis (PCA). Euclidean distance matching is used for identification and verification. The performance of the system is evaluated in real time in the laboratory environment, and the system achieves a recognition rate (RR) of 99.0% and an equal error rate (EER) of about 0.84% for 50 subjects. The performance of the system is also evaluated for the eXtended Multi Modal Verification for Teleservices and Security (XM2VTS) database, and the system achieves a recognition rate of 100% an equal error rate (EER) of about 0.23%.     

信息熵加权的HOG特征提取算法研究

针对人脸图像中不同部位所含的信息熵不同,对识别的影响程度不同等因素,提出了一种信息熵加权的HOG特征提取方法。该算法将待识别的人脸图像进行分块,对分块后的图像进行HOG特征提取,计算每块图像所含的信息熵作为权重系数加到各个分块中形成新的HOG特征,通过PCA算法对特征进行降维,得到信息熵加权的HOG特征。通过在ORL和YALE实验结果表明,该算法相较于其他传统识别方法具有更高的识别精度和准确度,并且对于人脸在光照、姿态表情等干扰因素下均具有良好的有效性和鲁棒性。     

基于Gabor系数分块统计和自适应特征选择的人脸描述与识别

提出一种新的人脸描述及识别方法,首先对归一化后的人脸图像进行多方向多尺度Gabor变换;然后对人脸区域进行分块,以块为单位统计Gabor系数的均值和方差,求得块特征矢量(block feature vector,BFV),按先行后列的顺序将各块的BFV拼接,构成整幅人脸图像特征矢量(face feature vector,FFV).在分类器设计阶段,引入两两比对和投票机制,用多个两类分类器组合成多类分类器.在训练某个具体的两类分类器时,根据隶属训练样本计算FFV中每项的分辨力,以分辨力大小为依据选出最优特征子集(best subset feature vector,BSFV).基于Yale人脸数据集展开实验,与已发表的算法和结果进行对比,证明了该方法的有效性.     

基于免疫粒子群的P2P协议识别方法

为了解决基于统计特征的P2P协议识别中,因特征选择不当而引起的识别准确率低的问题,采用免疫粒子群算法(Immune-PSO)选取最优特征子集,选择出最能区分P2P协议的特征子集。实验结果表明,该算法较标准粒子群算法具有更高的全局搜索能力,能更准确地找出最优特征子集,该方法能有效地提高协议的识别率,对常见的P2P协议如BitTorrent、eMule等有高达90%的识别率。     

Two-dimensional discriminant transform for face recognition

This paper develops a new image feature extraction and recognition method coined two-dimensional linear discriminant analysis (2DLDA). 2DLDA provides a sequentially optimal image compression mechanism, making the discriminant information compact into the up-left corner of the image. Also, 2DLDA suggests a feature selection strategy to select the most discriminative features from the corner. 2DLDA is tested and evaluated using the AT&T face database. The experimental results show 2DLDA is more effective and computationally more efficient than the current LDA algorithms for face feature extraction and recognition.     

基于EmbeddedMATLAB函数模块的图像相似度的实现

人脸识别算法的应用很广,这要求识别人脸的精确度要高,可通过计算待识别的图像与图像库中与之匹配的图像间的相似度来衡量是否准确识别。整个系统包括人脸识别的PCA算法的实现,主要是图像相似度的实现,通过采用Simulink模块库中的用户自定义模块--Embedded MATLAB函数模块来实现相似度算法。     

基于三角函数迭代的视频数据特征提取

在计算机视觉研究中,基于视频数据进行图像对象识别逐渐增多。针对视频数据 的特征提取,提出了一种基于三角函数迭代提取视频三维迭代轨迹特征的方法。该方法是考虑 视频数据的时间空间维度,利用三角函数构造三维动力系统,整体一次性进行视频段数据特征 的抽取,提取出一种近似混沌吸引子的三维特征点集,这种视频数据迭代特征实际上是迭代轨 迹点集合。以VidTIMIT 数据集进行人脸识别实验,发现增加初始迭代值的个数,减少迭代次 数后,提取出的特征点集合具有更好的效果。当VidTIMIT 的43 组559 个视频全部参与实验, 识别率达到88.16%,与现有文献中记载的其他方法相比,具有识别率高、计算时间少的特点, 初步证实了该三维视频迭代轨迹特征具有实用性,同时也值得进一步研究验证与分析。     

Facial expression recognition – A real time approach

Face localization, feature extraction, and modeling are the major issues in automatic facial expression recognition. In this paper, a method for facial expression recognition is proposed. A face is located by extracting the head contour points using the motion information. A rectangular bounding box is fitted for the face region using those extracted contour points. Among the facial features, eyes are the most prominent features used for determining the size of a face. Hence eyes are located and the visual features of a face are extracted based on the locations of eyes. The visual features are modeled using support vector machine (SVM) for facial expression recognition. The SVM finds an optimal hyperplane to distinguish different facial expressions with an accuracy of 98.5%.     

基于积分投影的人脸图像的特征提取

人脸识别是模式识别领域内的重要课题,有着十分广泛的应用前景,人脸特征的自动提取是人脸自动识别过程中重要的一步。该文采用基于人脸几何特征的方法,首先通过边缘检测和阈值技术对人脸图像进行预处理;然后分别采用水平和垂直积分投影的方法确定人脸轮廓,最后利用人脸特征的先验知识,提取出特征点。实验结果表明该人脸特征提取系统能有效地提取头部轮廓和人脸的主要特征点,实现简单,效率高,特别适合于标准证件类型的黑白照的识别。