基于自适应预处理和PCA的人脸识别方法的研究

针对传统预处理方法在特征提取之前不能对人脸图像进行局部化处理,不能分析出感兴趣区域及受背景环境影响等缺点,提出一种人脸图像的自适应预处理方法。该方法通过二维Gabor滤波器从人脸图像中确定人眼位置,通过图像分割算法提取出感兴趣区域,缩放图像,运用主分量分析方法进行特征提取,通过二维最小近邻分类法进行分类,从而完成人脸识别过程。实验结果表明,基于自适应预处理的人脸识别方法能够有效去除头发、脖子、肩及与人脸无关的部分,提高了人脸识别率,且对一定的平移、旋转、尺度变化和表情有良好的鲁棒性。     

利用小波和矩进行基于形状的图象检索

形状是图象中目标的重要特征,基于形状的图象检索近来在基于内容的图象库系统和管理和应用中得到越来越多的重视。现已研制的系统存在两个问题。一是性能的不稳定性;二是相对平移,旋转和尺度变换的变化性,针对上问题,该文提出了一种新的基于形状的图象检索算法。此算法先对亮度图象图象进行小波模极大值变换以得到多尺度的边界图象,再利用７个不变矩提取每一尺度边界图象的特征,所有尺度上的矩共同组成图象的特征向量。图象的     

基于M ATLAB 的第五套人民币面值识别系统设计

To solve the problems of accuracy and speed of the fifth set of RMB face value recognition, a design method of RMB face value recognition system based on MATLAB and image processing technology is given. The collected banknote images are pro- cessed by five modules: rotation correction, image preprocessing, image positioning, face value cutting and face value recognition. The experimental results show that the system can accurately calculate the total amount of money input while recognizing the face value of banknotes quickly in real time.     

Discriminability and reliability indexes: Two new measures to enhance multi-image face recognition

In order to handle complex face image variations in face recognition, multi-image face recognition has been proposed, instead of using a single still-image-based approach. In many practical scenarios, multiple images can be easily obtained in enrollment and query stages, for example, using video. By assessing these images, a good “quality” image(s) will be selected for recognition using conventional still-image-based recognition algorithms so that the recognition performance can be improved. However, existing methods do not fully utilize all images information. In this paper, two new measurements, namely discriminability index (DI) and reliability index (RI), are proposed to evaluate the enrolled and query images, respectively. By considering the distribution of enrolled images from individuals, the discriminability index of each image is calculated and a weight is assigned. For testing images, a reliability index is calculated based on matching quality between the testing images and enrolled images. If the reliability index of a testing image is small, the testing image will be discarded as it may degrade the recognition performance. To evaluate and demonstrate the use of DI and RI, we adopt the combining classifier method with eigenface representations in input and kernel feature spaces. CMU-PIE, YaleB and FRGC V2.0 databases are used for experiments. Experimental results show that the recognition performance, with three popular combination rules, can be increased by more than 10% on average using DI and RI.     

监控视频下的人脸图像质量分析技术

监控场景下人脸图像质量分析技术的研究具有重要意义.由于监控视频采集到人脸模糊、头部角度不正、被其他物体遮挡等的低质量图像进入识别系统会造成识别准确率下降.为了解决上述问题,通过实验,研究了监控场景下影响图像质量的两个重要因素:人脸角度、图像清晰度.设计了基于聚类的人脸图像质量分析算法,提出了人脸图像质量分数的计算公式,实验表明该技术能够有效过滤监控视频下采集到的低质量图像,进而提高人脸识别系统的准确率.     

一种高性能的遥感图像目标快速筛选算法

提出一种基于双向部分Hausdorff距离的特征点匹配方法，目的是提高从遥感图像中自动筛选感兴趣目标的精度和速度。这种方法通过构造图像多分辨率金字塔，按照一定间隔旋转模型，将双向部分Hausdorff距离作为平移矢量的函数来计算并采用多种加速机制等途径，依次确定平移、旋转和尺度变换参数，较好地解决了图像与模型之间存在平移、旋转、尺度变换时对应关系的求解问题。关于实际遥感图像的大量实验表明，该方法具有计算简便快捷、对边缘位置误差稳健、可工作于遮挡、阴影、复杂背景存在的环境中的优点。     

Sketch-based image matching Using Angular partitioning

This work presents a novel method for image similarity measure, where a hand-drawn rough black and white sketch is compared with an existing data base of full color images (art works and photographs). The proposed system creates ambient intelligence in terms of the evaluation of nonprecise, easy to input sketched information. The system can then provide the user with options of either retrieving similar images in the database or ranking the quality of the sketch against a given standard, i.e., the original image model. Alternatively, the inherent pattern-matching capability of the system can be utilized to allow detection of distortion in any given real time-image sequences in vision-driven ambient intelligence applications. The proposed method can cope with images containing several complex objects in an inhomogeneous background. Two abstract images are obtained using strong edges of the model image and the morphologically thinned outline of the sketched image. The angular-spatial distribution of pixels in the abstract images is then employed to extract new compact and effective features using the Fourier transform. The extracted features are rotation and scale invariant and robust against translation. Experimental results from seven different approaches confirm the efficacy of the proposed method in both the retrieval performance and the time required for feature extraction and search.     

基于球面谐波基图像的任意光照下的人脸识别

提出了一种基于球面谐波基图像的光照补偿算法,用以在任意光照条件下进行人脸识别.算法分两步进行：光照估计和光照补偿.基于人脸形状大致相同和每个人脸的反射率基本相等的假设,首先估计了输入人脸图像光照的9个低频谐波系数.根据光照估计的结果,提出了两种光照补偿方法：纹理图像和差图像.纹理图像为输入图像与其光照辐照图之商,与输入图像的光照条件无关.差图像为输入图像与平均人脸在相同光照下的图像之差,通过减去平均人脸在相同光照下的图像,减弱了光照的影响.在CMU-PIE人脸库和Yale B人脸库上的实验表明,通过光照补偿,不同光照下人脸图像识别率有了很大提高.     

Tensor-Based AAM with Continuous Variation Estimation: Application to Variation-Robust Face Recognition

The active appearance model (AAM) is a well-known model that can represent a non-rigid object effectively. However, the fitting result is often unsatisfactory when an input image deviates from the training images due to its fixed shape and appearance model. To obtain more robust AAM fitting, we propose a tensor-based AAM that can handle a variety of subjects, poses, expressions, and illuminations in the tensor algebra framework, which consists of an image tensor and a model tensor. The image tensor estimates image variations such as pose, expression, and illumination of the input image using two different variation estimation techniques: discrete and continuous variation estimation. The model tensor generates variation-specific AAM basis vectors from the estimated image variations, which leads to more accurate fitting results. To validate the usefulness of the tensor-based AAM, we performed variation-robust face recognition using the tensor-based AAM fitting results. To do, we propose indirect AAM feature transformation. Experimental results show that tensor-based AAM with continuous variation estimation outperforms that with discrete variation estimation and conventional AAM in terms of the average fitting error and the face recognition rate.     

An integrated automatic face detection and recognition system

This paper proposes an integrated system for unconstrained face recognition in complex scenes. The scale and orientation tolerant system comprises a face detector followed by a recognizer. Given a color input image of a person, the face detector encloses the face from the complex scene within a circular boundary, and locates the position of the nose. A radial grid mapping centered on the nose is then performed to extract a feature vector within the boundary. The feature vector is input to a radial basis function neural network classifier for face identification. The proposed face detector achieved an average detection rate of 95.8% while the face recognizer achieved an average recognition rate of 97.5% on a database of 21 persons with variations in scale, orientation, natural illumination and background. The two modules were combined to form an automatic face recognition system that was evaluated in the context of a security system using a video database of 21 users and 10 intruders, acquired in an unconstrained environment. A recognition rate of 93.5% with 0% false acceptance rate was achieved.     

A Bayesian approach for object classification based on clusters of SIFT local features

Several methods have been presented in the literature that successfully used SIFT features for object identification, as they are reasonably invariant to translation, rotation, scale, illumination and partial occlusion. However, they have poor performance for classification tasks. In this work, SIFT features are used to solve object class recognition problems in images using a two-step process. In its first step, the proposed method performs clustering on the extracted features in order to characterize the appearance of the different classes. Then, in the classification step, it uses a three layer Bayesian network for object class recognition. Experiments show quantitatively that clusters of SIFT features are suitable to represent classes of objects. The main contributions of this paper are the introduction of a Bayesian network approach in the classification step to improve performance in an object class recognition task, and a detailed experimentation that shows robustness to changes in illumination, scale, rotation and partial occlusion.     

用组合不变量进行模式识别的一种新方法

用与目标的位置、大小、方向和其他变化无关的特征来识别目标是模式识别领域的一个热点。现存的基于不变特征的二维模式识别方法在目标被模糊了情况下都无法精确识别。本文提出了一种可解决上述问题的新的模式识别方法。该方法用组合不变量作为图像特征,以加权规格化互相关作为分类技术。在分类过程中,使用每一类的所有原型的第k个特征的类内标准方差的均值作为加权因子以提高识别率。对头像的数字试验证实了组合不变量特征对图像的平移、伸缩、旋转和模糊变换的不变性和该模式识别方法的可行性。     

基于自适应网格变形的图像编辑算法

提出一套基于自适应网格变形的图像编辑算法框架,包括图像中特征物的平移、旋转和变形,以及保持特征物的任意几何边界图像适应.该算法将图像表示为基于图像特征的自适应三角网格,由此将图像编辑问题转换为带约束的网格变形问题.网格变形由一个二次型能量函数所控制,特征物的平移、旋转和变形可以表述为该能量优化问题的约束;代表特征物的三角网格在网格变形过程中只允许发生刚性变换.该能量优化问题的全局最优解可以通过求解1个或多个稀疏方程组得到.实验结果表明,该算法效果理想、鲁棒性好、运行效率高,可以有效地应用于图像处理软件中.     

Affine-invariant recognition of gray-scale characters using globalaffine transformation correlation

Describes a technique of gray-scale character recognition that offers both noise tolerance and affine-invariance. The key ideas are twofold. First is the use of normalized cross-correlation as a matching measure to realize noise tolerance. Second is the application of global affine transformation (GAT) to the input image so as to achieve affine-invariant correlation with the target image. In particular, optimal GAT is efficiently determined by the successive iteration method using topographic features of gray-scale images as matching constraints. We demonstrate the high matching ability of the proposed GAT correlation method using gray-scale images of numerals subjected to random Gaussian noise and a wide range of affine transformation. Moreover, extensive recognition experiments show that the achieved recognition rate of 94.3 percent against rotation within 30 degrees, scale change within 30 percent, and translation within 20 percent of the character width along with random Gaussian noise is sufficiently high compared to the 42.8 percent offered by simple correlation     

面向大姿态人脸识别的正面化形变场学习

目的 人脸识别已经得到了广泛应用,但大姿态人脸识别问题仍未完美解决。已有方法或提取姿态鲁棒特征,或进行人脸姿态的正面化。其中主流的人脸正面化方法包括2D回归生成和3D模型形变建模,前者能够生成相对自然真实的人脸,但会引入额外的噪声导致图像信息的扭曲;后者能够保持原始的人脸结构信息,但生成过程是基于物理模型的,不够自然灵活。为此,结合2D和3D方法的优势,本文提出了基于由粗到细形变场的人脸正面化方法。方法 该形变场由深度网络以2D回归方式学得,反映的是不同视角人脸图像像素之间的语义级对应关系,可以类3D的方式实现非正面人脸图像的正面化,因此该方法兼具了2D正面化方法的灵活性与3D正面化方法的保真性,且借鉴分步渐进的思路,本文提出了由粗到细的形变场学习框架,以获得更加准确鲁棒的形变场。结果 本文采用大姿态人脸识别实验来验证本文方法的有效性,在MultiPIE(multi pose, illumination, expressions)、LFW(labeled faces in the wild)、CFP(celebrities in frontal-profile in the wild)...