基于WSSRC单样本人脸识别及样本扩充方法研究

由于传统的SRC方法的实时性不强、单样本条件下算法性能低等缺点,提出了融合全局和局部特征的加权超级稀疏表示人脸识别方法(WSSRC),同时采用一种三层级联的虚拟样本产生方法获取冗余样本,将生成的多种表情和多种姿态的新样本当成训练样本,运用WSSRC算法进行人脸识别分类。在单样本的情况下,实验证实在ORL人脸库上该方法比传统的SRC方法提高了15.53%的识别率,使用在FERET 人脸库上则提高7.67%。这样的方法与RSRC 、SSRC、DMMA、DCT-based DMMA、I-DMMA相比,一样具备较好的识别性能。     

低照度条件下超分辨率人脸图像采集系统设计与实现

在计算机技术高速发展的时代,多平台计算机视觉库随之产生。OpenCV作为一种开源代码的计算机视觉库,以可兼容多平台、接口广泛的特点被广泛运用各个领域。在低照度条件下,会出现光照环境差异过大或光线不足等情况,导致传统图像采集系统不能采集高质量的人脸图像,局限性较差。提出基于OpenCV在C 环境配置下运用三维人脸识别技术算法,设计一套低照度条件下超分辨率人脸图像采集系统。实验证明,该设计方案具有实时(对焦速度快)、快速(单张采集0.05秒)、准确(面部识别率99.3%)等特点,能够充分满足低照度条件下超分辨率人脸图像采集的需求。     

一种自适应的Gabor图像特征抽取和权重选择的人脸识别方法

 为了克服光照、表情变化等因素对人脸识别的影响,本文提出了一种自适应的Gabor图像特征抽取和权重选择的人脸识别方法.该方法首先把每幅人脸图像经过Gabor小波变换后得到的40个不同尺度和方向下的图像都看作是独立的样本,再把不同人脸中的同一尺度和方向的变换结果进行特征重组,得到40个独立地新特征矩阵.为了增强对光照、表情变化的鲁棒性,每一新特征矩阵的识别贡献被本文所提出的自适应权重方法计算得到.其次,对每一新特征矩阵采用离散余弦变化进行降维,并采用了鉴别力量分析方法来选取最有鉴别力的离散余弦变换系数作为特征向量.最后,抽取线性鉴别分析特征进行识别.大量的实验证明了本文所提方法的有效性.     

基于微观本义复原的人脸图像光照正规化

为解决变光照下人脸识别的识别率低,光照正规化算法复杂.不易实现的问题,提出一个新的光照正规化方法一微观本义复原,即通过微观邻域上像素本义特征的定义,将整体图像上变光照下的非线性灰度变化转化为微观邻域内的线性变化,一定程度上避免了包括图像自身结构在内的不确定因素对图像复原的不利影响;并以邻域内的本义特征为光照不敏感特征,对本义特征进行结构编码,并用最小二乘法拟合编码值与光照方向之间的关系,最后根据得到的光照正规化参数复原图像.实验结果表明,该方法算法简单,易于实现,能适应实时的人脸识别系统,在光照变化90.以内的Yale B库的平均识别率可达94.1%.     

Nonparametric subspace analysis fused to 2DPCA for face recognition

Two-dimensional principal component analysis (2DPCA) is one of the representative techniques for image representation and recognition. However, keen storage requirements and computational complexity consist in 2DPCA. Meanwhile, the performance of 2DPCA is delicate in illumination variations. Nonparametric subspace analysis (NSA) is a subspace learning method that can reduce dimensionality and identify local information for discrimination, so that it can make 2DPCA perform well in illumination. Motivated by above facts, 2DPCA fused with NSA is implemented for face recognition, which can reduce dimensions of the 2DPCA feature vectors and enhance the contribution of principal components to face recognition. Experiments carried out on ORL, Yale B, and FERET facial databases show that valid recognition rates can be achieved by the proposed method compared to 2DPCA, 2DPCA plus PCA, LDA methods and demonstrate promising abilities against illumination variations.     

‘Symmetrical face’ based improved LPP method for face recognition

As one of the most important branches of pattern recognition and computer vision, face recognition has more and more become the focus of researches. In real word applications, the face image might have various changes owing to varying illumination, facial expression and poses, so we need sufficient training samples to convey these possible changes. However, most face recognition systems cannot capture many face images of every user for training, non-sufficient training samples have become one bottleneck of face recognition. In this paper, we propose to exploit the symmetry of the face to generate ‘symmetrical face’ samples and use an improved LPP method to perform classification. Experimental results show that our method can get a high accuracy.     

驾驶人眼睛区域的鲁棒性定位算法研究

疲劳驾驶是造成交通事故的重要原因. 通过机器视觉技术对眼睛动作和视线转移特征的分析可实现驾驶人疲劳状态的有效估计. 然而, 实际行车环境中光照条件的随机、快速变化以及驾驶人面部姿态的不确定性使得眼睛区域的鲁棒性定位变得异常困难. 为此, 本文引入基于点分布模型的主动形状模型(ASM)算法并针对其在实际行车环境中存在的问题提出了三点改进. 首先, 建立了基于自商图的局部纹理模型以克服光照变化的影响; 其次, 充分利用面部局部区域良好的聚类性, 建立了层叠式全局形状模型, 以适应驾驶人姿态的大角度偏转; 再次, 在行车过程中, 通过对驾驶人面部形状的在线学习实现模型参数分布特征的获取, 为ASM算法的配准提供了更加紧致的约束. 实验结果显示, 本文算法对光照和姿态变化具有较强的鲁棒性, 在驾驶人面部器官不发生自遮挡的情况下可实现眼睛区域的高精度配准.     

人脸图像光照预处理的一种新方法

人脸识别是图像分析和理解领域中最成功的应用之一,近年来得到了迅速的发展,但是阻碍人脸识别技术应用到实际中的瓶颈之一——光照问题,一直没能得到很好的解决。局部二值模式是最近发展起来的一种理论简单但功能强大的纹理分析算法,在计算机视觉等领域表现出良好的性能。将该纹理提取算法应用到图像预处理中并并利用大规模中国人脸图像数据库CAS-PEAL-R1来检验这种方法的有效性。实验结果表明:加入LBP纹理后,该方法能较好解决光照变化问题,提高识别性能。     

Hand gesture pointing location detection

Gesture recognition has been implemented in many systems for different purposes. The way it is used can be different from system to system. Few used them as touch objects using sensors while few use them as pointing object where the camera is in front of the hand, but the location is being pointed by human, can be detected only if the camera is behind that person. This paper presents an automatic system to detect the location on the wall where the user is currently pointing by his hand. This is done with the detection of head and both hands. The shoulders and elbows’ locations are identified using geometry analysis to understand the current gesture of the human and finally the direction where user hand is pointing would be detected. The face was detected using Haar classifier and shoulders were estimated using the rectangle method near the head. This method does not work in poor illumination conditions and is made to detect only one person at a time. Also user is supposed to wear half sleeve or sleeveless shirt for the better segmentation.     

Quaternion-based improved LPP method for color face recognition

In recent years, pattern recognition and computer vision have increasingly become the focus of research. Locality preserving projection (LPP) is a very important learning method in these two fields and has been widely used. Using LPP to perform face recognition, we usually can get a high accuracy. However, the face recognition application of LPP suffers from a number of problems and the small sample size is the most famous one. Moreover, though the face image is usually a color image, LPP cannot sufficiently exploit the color and we should first convert the color image into the gray image and then apply LPP to it. Transforming the color image into the gray image will cause a serious loss of image information. In this paper, we first use the quaternion to represent the color pixel. As a result, an original training or test sample can be denoted as a quaternion vector. Then we apply LPP to the quaternion vectors to perform feature extraction for the original training and test samples. The devised quaternion-based improved LPP method is presented in detail. Experimental results show that our method can get a higher classification accuracy than other methods.     

基于识别能力的人脸特征向量均衡

在人脸识别中,通常是把人脸图像投影在特征向量组成的空间中,特征向量决定了人脸识别的效果。同一人的人脸图像在好的特征向量上的投影应具有较集中的分布,反之亦然。从微观意义上提出了每个向量的识别能力的概念,通过计算识别能力的大小,选择具有较大的识别能力的特征向量。为了使每个向量在识别中发挥与其识别能力相对应的作用,根据每个向量的识别能力对它们进行均衡化处理,赋予不同权重,提高了识别率。通过在ORL、Yale人脸数据库上进行试验,验证了该方法的有效性。     

Face recognition based on PCA image reconstruction and LDA

Face recognition has become a research hotspot in the field of pattern recognition and artificial intelligence. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two traditional methods in pattern recognition. In this paper, we propose a novel method based on PCA image reconstruction and LDA for face recognition. First, the inner-classes covariance matrix for feature extraction is used as generating matrix and then eigenvectors from each person is obtained, then we obtain the reconstructed images. Moreover, the residual images are computed by subtracting reconstructed images from original face images. Furthermore, the residual images are applied by LDA to obtain the coefficient matrices. Finally, the features are utilized to train and test SVMs for face recognition. The simulation experiments illustrate the effectivity of this method on the ORL face database.     

Accuracy enhanced thermal face recognition

Human face recognition has been generally researched for the last three decades. Face recognition with thermal image has begun to attract significant attention gradually since illumination of environment would not affect the recognition performance. However, the recognition performance of traditional thermal face recognizer is still insufficient in practical application. This study presents a novel thermal face recognizer employing not only thermal features but also critical facial geometric features which would not be influenced by hair style to improve the recognition performance. A three-layer back-propagation feed-forward neural network is applied as the classifier. Traditional thermal face recognizers only use the indirect information of the topography of blood vessels like thermogram as features. To overcome this limitation, the proposed thermal face recognizer can use not only the indirect information but also the direct information of the topography of blood vessels which is unique for every human. Moreover, the recognition performance of the proposed thermal features would not decrease even if the hair of frontal bone varies, the eye blinks or the nose breathes. Experimental results show that the proposed features are significantly more effective than traditional thermal features and the recognition performance of thermal face recognizer is improved.     

Thermal 3D modeling system based on 3-view geometry

In this paper, we propose a novel thermal three-dimensional (3D) modeling system that includes 3D shape, visual, and thermal infrared information and solves a registration problem among these three types of information. The proposed system consists of a projector, a visual camera and, a thermal camera (PVT). To generate 3D shape information, we use a structured light technique, which consists of a visual camera and a projector. A thermal camera is added to the structured light system in order to provide thermal information. To solve the correspondence problem between the three sensors, we use three-view geometry. Finally, we obtain registered PVT data, which includes visual, thermal, and 3D shape information. Among various potential applications such as industrial measurements, biological experiments, military usage, and so on, we have adapted the proposed method to biometrics, particularly for face recognition. With the proposed method, we obtain multi-modal 3D face data that includes not only textural information but also data regarding head pose, 3D shape, and thermal information. Experimental results show that the performance of the proposed face recognition system is not limited by head pose variation which is a serious problem in face recognition.     

基于DSP的静脉识别系统

提出了一种基于ADSP-BF533的静脉识别系统设计方法。整个系统由静脉图像采集、静脉图像处理和识别两个模块组成。静脉图像由普通黑白模拟摄像机在近红外光照下获取;静脉图像处理和识别的核心单元是一颗ADI生产的Blackfin 533 DSP(ADSP-BF533),其速度可以满足后端处理的需要,可以实现静脉图像细化、特征提取及识别等功能。     

基于方向滤波和自商图像的人脸光照补偿

为了实时处理恶劣光照条件下的人脸图像,在自商图像的基础上,提出了一种新的人脸光照补偿的方法.该方法首先在人脸三维光照简化模型的基础上,利用方向滤波削弱附着阴影.然后结合高斯低通滤波器,进一步削弱投射阴影.两者非线性结合,显著改善光照图像的质量.利用Yale B数据库提供的光照图像,在10万人脸数据库系统中进行测试,结果表明在光照条件恶劣的情况下能显著提高识别率.     

A two-phase face recognition method in frequency domain

In this paper, we propose a two-phase face recognition method in frequency domain using discrete cosine transform (DCT) and discrete Fourier transform (DFT). The absolute values of DCT coefficients or DFT amplitude spectra are used to represent the face image, i.e. the transformed image. Then a two-phase face classification method is applied to the transformed images. This method is as follows: its first phase uses the Euclidean distance formula to calculate the distance between a test sample and each sample in the training sets, and then exploits the Euclidean distance of each training sample to determine K nearest neighbors for the test sample. Its second phase represents the test sample as a linear combination of the determined K nearest neighbors and uses the representation result to perform classification. In addition, we use various numbers of DCT coefficients and DFT amplitude spectra to test the effect on our algorithms. The experimental results show that our method outperforms the two-phase face recognition method based on space domain of face images.     

云计算环境下人脸表情智能识别改进技术研究

随着人脸识别技术的开发,对于如何提高人脸表情智能识别改进技术的研究也越来越多;如何提高人脸识别的准确度和完整度是当前发展的主要需要,而计算机云计算功能在人脸识别中的应用在一定程度上解决了此问题;通过改进细菌觅食算法,再将其应用到主要成分分析算法对图像基本特征进行提取分析;通过以上的算法输入计算机网络云储存当中,实现云计算技术在人脸识别中的应用;文章将通过对于算法部署函数的办法进行图片解析工作,并且利用智能人脸识别软件对图像进行抽丝、分类、匹配等工作进行功能状态进行测试;实验结果表明利用云计算技术通过连接网络云计算系统可以对目前的人脸识别以及分类做到更高的准确性和适应性。     

Affine invariant feature extraction algorithm based on multiscale autoconvolution combining with texture structure analysis

Affine invariant feature extraction has been one of the key issues for object recognition, especially for the images captured under the variable environments. Considering that multiscale autoconvolution feature (MSA), which has the prominent comprehensive performance, is very sensitive to illumination change, a novel algorithm of extracting affine invariant feature is proposed based on the MSA transform combining with texture structure analysis. Firstly, a new MSA feature is extracted from texture structure map of the image which is computed based on local binary pattern theory. And then the original image based MSA and the texture map based MSA are combined to a new feature using generalized canonical correlation analysis, called TFMSA. This new feature represents much more image information than the traditional one and is performed in various object recognition tasks. The experimental results indicate that the new TFMSA not only conquers the defect of the traditional MSA, but also has good adaptability for a certain range of viewing angles, partial occlusion, uniform and non-uniform illumination changes. The recognition accuracy of the new feature is superior to MSA and other improved methods.