图像多模态扰动的人脸识别方法

为了克服因人脸图像检测引起的配准不稳定性和小样本引起的维数灾难,由一副二维人脸图像通过上下左右平移生成4个图像,把生成的图像与原来的图像一起加入训练样本集,构成新的训练图像集。基于二维图像,结合图像局部结构信息,设计了准则函数,获得双投影矩阵,抽取人脸特征。对待识别人脸图像,由它的扰动图像设计识别方法。与传统的人脸识别方法相比,该方法的识别效果更好;Yale和ORL人脸数据库上的实验结果验证了该方法的有效性。     

可变光照条件下的人脸识别

主要解决人脸识别中因光照变化导致误识或者拒识的问题。使用DOG（高斯差分变换）对原始人脸图像样本集（A）进行处理,将滤波后的人脸图像样本集（B）加入到原始样本集（A）中,采用了新的方法将样本集A和B进行融合,则既对极端光照条件下人脸图像进行了矫正,又不影响正常光照条件下的人脸识别。在分类阶段,引入了SRC（Sparse Representation Classification）分类器代替传统分类器,提升了在低错误接收率下的识别率,改善因光照剧烈变换而导致的无法识别或者误识的情况。在公开人脸库Yale-B、CMU-PIE以及ORL上的实验结果表明,该方法在不同光照条件下可以提高识别率,改善拒识和误识情况。     

Illumination invariant face recognition

The appearance of a face will vary drastically when the illumination changes. Variations in lighting conditions make face recognition an even more challenging and difficult task. In this paper, we propose a novel approach to handle the illumination problem. Our method can restore a face image captured under arbitrary lighting conditions to one with frontal illumination by using a ratio-image between the face image and a reference face image, both of which are blurred by a Gaussian filter. An iterative algorithm is then used to update the reference image, which is reconstructed from the restored image by means of principal component analysis (PCA), in order to obtain a visually better restored image. Image processing techniques are also used to improve the quality of the restored image. To evaluate the performance of our algorithm, restored images with frontal illumination are used for face recognition by means of PCA. Experimental results demonstrate that face recognition using our method can achieve a higher recognition rate based on the Yale B database and the Yale database. Our algorithm has several advantages over other previous algorithms: (1) it does not need to estimate the face surface normals and the light source directions, (2) it does not need many images captured under different lighting conditions for each person, nor a set of bootstrap images that includes many images with different illuminations, and (3) it does not need to detect accurate positions of some facial feature points or to warp the image for alignment, etc.     

Homomorphic filtering based illumination normalization method for face recognition

The appearance of a face image is severely affected by illumination conditions that will hinder the automatic face recognition process. To recognize faces under varying lighting conditions, a homomorphic filtering-based illumination normalization method is proposed in this paper. In this work, the effect of illumination is effectively reduced by a modified implementation of homomorphic filtering whose key component is a Difference of Gaussian (DoG) filter, and the contrast is enhanced by histogram equalization. The resulted face image is not only reduced illumination effect but also preserved edges and details that will facilitate the further face recognition task. Among others, our method has the following advantages: (1) neither does it need any prior information of 3D shape or light sources, nor many training samples thus can be directly applied to single training image per person condition; and (2) it is simple and computationally fast because there are mature and fast algorithms for the Fourier transform used in homomorphic filter. The Eigenfaces method is chosen to recognize the normalized face images. Experimental results on the Yale face database B and the CMU PIE face database demonstrate the significant performance improvement of the proposed method in the face recognition system for the face images with large illumination variations.     

人脸识别率与图像分辨率关系的比较分析

为在低分辨率图像中提高人脸识别率,从实际应用角度出发,分析研究图像分辨率与人脸识别率间的关系,在此基础上,采用主成分分析方法,对3种数据库中具有不同分辨率的人脸图像进行识别。仿真实验结果表明,该方法能在人脸图像分辨率较低的情况下获得与高分辨率图像基本一致的识别效果,且同时兼顾识别率及识别效率。     

High-speed face recognition using self-adaptive radial basis function neural networks

In this work, we have proposed a self-adaptive radial basis function neural network (RBFNN)-based method for high-speed recognition of human faces. It has been seen that the variations between the images of a person, under varying pose, facial expressions, illumination, etc., are quite high. Therefore, in face recognition problem to achieve high recognition rate, it is necessary to consider the structural information lying within these images in the classification process. In the present study, it has been realized by modeling each of the training images as a hidden layer neuron in the proposed RBFNN. Now, to classify a facial image, a confidence measure has been imposed on the outputs of the hidden layer neurons to reduce the influences of the images belonging to other classes. This process makes the RBFNN as self-adaptive for choosing a subset of the hidden layer neurons, which are in close neighborhood of the input image, to be considered for classifying the input image. The process reduces the computation time at the output layer of the RBFNN by neglecting the ineffective radial basis functions and makes the proposed method to recognize face images in high speed and also in interframe period of video. The performance of the proposed method has been evaluated on the basis of sensitivity and specificity on two popular face recognition databases, the ORL and the UMIST face databases. On the ORL database, the best average sensitivity (recognition) and specificity rates are found to be 97.30 and 99.94%, respectively using five samples per person in the training set. Whereas, on the UMIST database, the above quantities are found to be 96.36 and 99.81%, respectively using eight samples per person in the training set. The experimental results indicate that the proposed method outperforms some of the face recognition approaches.     

Face recognition based on 3D ridge images obtained from range data

In this paper, we present an approach for 3D face recognition from frontal range data based on the ridge lines on the surface of the face. We use the principal curvature, k_max, to represent the face image as a 3D binary image called ridge image. The ridge image shows the locations of the ridge points around the important facial regions on the face (i.e., the eyes, the nose, and the mouth). We utilized the robust Hausdorff distance and the iterative closest points (ICP) for matching the ridge image of a given probe image to the ridge images of the facial images in the gallery. To evaluate the performance of our approach for 3D face recognition, we performed experiments on GavabDB face database (a small size database) and Face Recognition Grand Challenge V2.0 (a large size database). The results of the experiments show that the ridge lines have great capability for 3D face recognition. In addition, we found that as long as the size of the database is small, the performance of the ICP-based matching and the robust Hausdorff matching are comparable. But, when the size of the database increases, ICP-based matching outperforms the robust Hausdorff matching technique.     

Facial deblur inference using subspace analysis for recognition of blurred faces

This paper proposes a novel method for recognizing faces degraded by blur using deblurring of facial images. The main issue is how to infer a Point Spread Function (PSF) representing the process of blur on faces. Inferring a PSF from a single facial image is an ill-posed problem. Our method uses learned prior information derived from a training set of blurred faces to make the problem more tractable. We construct a feature space such that blurred faces degraded by the same PSF are similar to one another. We learn statistical models that represent prior knowledge of predefined PSF sets in this feature space. A query image of unknown blur is compared with each model and the closest one is selected for PSF inference. The query image is deblurred using the PSF corresponding to that model and is thus ready for recognition. Experiments on a large face database (FERET) artificially degraded by focus or motion blur show that our method substantially improves the recognition performance compared to existing methods. We also demonstrate improved performance on real blurred images on the FRGC 1.0 face database. Furthermore, we show and explain how combining the proposed facial deblur inference with the local phase quantization (LPQ) method can further enhance the performance.     

Elastic shape-texture matching for human face recognition

In this paper, a novel, elastic, shape-texture matching method, namely ESTM, for human face recognition is proposed. In our approach, both the shape and the texture information are used to compare two faces without establishing any precise pixel-wise correspondence. The edge map is used to represent the shape of an image, while the texture information is characterized by both the Gabor representations and the gradient direction of each pixel. Combining these features, a shape-texture Hausdorff distance is devised to compute the similarity of two face images. The elastic matching is robust to small, local distortions of the feature points such as those caused by facial expression variations. In addition, the use of the edge map, Gabor representations and the direction of the image gradient can all alleviate the effect of illumination to a certain extent.With different databases, experimental results show that our algorithm can always achieve a better performance than other face recognition algorithms under different conditions, except when an image is under poor and uneven illumination. Experiments based on the Yale database, AR database, ORL database and YaleB database show that our proposed method can achieve recognition rates of 88.7%, 97.7%, 78.3% and 89.5%, respectively.     

基于独立分量分析和遗传算法的人脸性别分类

性别是人脸反映的一个重要信息,通过人脸图像实现性别自动分类对大型人脸数据库的检索和识别具有重要意义。提出了一种新的结合独立分量分析(ICA)和遗传算法(GA)的人脸性别分类方法。首先采用快速独立分量分析方法(FastICA)提取人脸图像的独立基图像和投影向量,获得人脸的低维表征;然后通过遗传算法从该低维空间中选择对性别分类有利的特征子集;最后采用支持向量机进行分类。将ICA的空间局部特征提取功能、遗传算法快速寻优的特征选择功能以及SVM的强分类能力有机地结合起来。实验表明,该方法取得了很好的分类性能。     

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.     

基于TI-SPCA的人脸自动对齐及识别框架

随着人脸识别算法在众多应用领域的迅猛发展,作为人脸检测和人脸识别中间步骤的人脸对齐算法日益受到重视。针对平面内的人脸图像旋转问题,提出一个基于TI-SPCA（Transformation Invariant Symmetrical Principal Components Analysis）的人脸自动对齐方法及其识别框架。不同于传统的人眼对齐方法,TI-SPCA通过最小化重构图像和扭曲图像之间的误差得到一个旋转不变的特征空间,最终实现无人为干涉的全自动对齐。为了将其性能与人眼对齐方法的性能进行比较,并展示其优势,文中分别在ORL数据库和FERET数据库上通过两种不同对齐方法的输出图像从视觉效果上直观地展现。进一步地,为了验证对齐后的图像在识别算法中的有效性,结合三种距离函数和四种局部算子进行了对比实验,实验结果表明了基于TI-SPCA的全自动对齐方法在人脸识别中的有效性。     

脱机手写汉字识别的最优采样特征新方法

在脱机手写汉字识别中，笔画形变是造成识别率下降的主要原因，减少笔画形变的影响是提高脱机手写汉字识别率的关键。针对上述问题，提出了最优采样特征。该特征以目前被广泛应用的方向线素特征为基础，在一定的约束条件下，通过移动采样点的位置，可以适应笔画的形变。从而减少特征的类内方差，提高特征的可分性，改进了识别性能。通过在THCHR样本集上进行实验，并对最优采样特征和方向线素特征的实验结果进行比较，验证了最优采样特征的识别率优于方向线索特征。     

基于FABEMD的变化光照下人脸识别方法

在光照变化的环境下,人脸识别因受到光照强度和方向的非线性干扰而变得困难重重。在人脸局部区域,光照的变化比较缓慢,而皮肤对光照的反射率特征变化比较快,可以认为光照变化是低频信号,而人脸本质特征是高频信号。FABEMD是一种快速自适应的BEMD（Bidimensional Empirical Mode Decomposition,二维经验模式分解）方法,它能够将图像分解为不同尺度的高频图像和低频图像,高频图像代表了人脸皮肤细节纹理特征,而低频图像则代表了轮廓特征。但是并不能定量判别什么样的高频信号以及多少高频信号能够用来消除光照影响,所以提出了两种衡量高频细节信息量的方法,将这些信息量的相对值来推算融合不同尺度的高频信号权重系数。基于Yale B人脸数据库的实验数据证明了所提方法能够取得很好的识别效果。     

二维及三维多模人脸数据库构建

基于图像的二维人脸识别技术日趋成熟,但仍受光照、姿态和表情等变化的影响。利用三维人脸模型提高人脸识别性能并将其应用于实际成为近几年学术界的研究趋势。本文提出了SWJTU-MF多模人脸数据库(SWJTU multimodal face database, SWJTU-MF Database),包 含200个中性表情中国人的4种人脸样本数据,包括可见光图像、二维视频序列、三维人脸(高精度)和立体视频序列。本文首先分类介绍现有的三维人脸识别算法,然后概述相关的多模人脸数据库,接着提出SWJTU-MF多模人脸数据库,并说明数据库的采集装置、采集环境、采集过程及数据内容,随后简要展示数据标准化过程。最后讨论本数据库面向的应用研究,并给出SWJTU-MF建议的评测协议。     

Face recognition under arbitrary illumination using illuminated exemplars

Recently, the importance of face recognition has been increasingly emphasized since popular CCD cameras are distributed to various applications. However, facial images are dramatically changed by lighting variations, so that facial appearance changes caused serious performance degradation in face recognition. Many researchers have tried to overcome these illumination problems using diverse approaches, which have required a multiple registered images per person or the prior knowledge of lighting conditions. In this paper, we propose a new method for face recognition under arbitrary lighting conditions, given only a single registered image and training data under unknown illuminations. Our proposed method is based on the illuminated exemplars which are synthesized from photometric stereo images of training data. The linear combination of illuminated exemplars can represent the new face and the weighted coefficients of those illuminated exemplars are used as identity signature. We make experiments for verifying our approach and compare it with two traditional approaches. As a result, higher recognition rates are reported in these experiments using the illumination subset of Max-Planck Institute face database and Korean face database.     

Unified probabilistic models for face recognition from a single example image per person

This paper presents a new technique of unified probabilistic models for face recognition from only one single example image per person. The unified models, trained on an obtained training set with multiple samples per person, are used to recognize facial images from another disjoint database with a single sample per person. Variations between facial images are modeled as two unified probabilistic models: within-class variations and between-class variations. Gaussian Mixture Models are used to approximate the distributions of the two variations and exploit a classifier combination method to improve the performance. Extensive experimental results on the ORL face database and the authors‘ database (the ICT-JDL database) including totally 1,750 facial images of 350 individuals demonstrate that the proposed technique, compared with traditional eigenface method and some well-known traditional algorithms, is a significantly more effective and robust approach for face recognition.     

人脸图像分块分色的特征抽取方法

提出一种简单且识别率高的分块分色的人脸图像特征抽取方法。该方法将人脸图像按行均等分块,在HSV颜色空间抽取每个子块图像的每维颜色空间模式值的均值和方差特征,用改进马氏距离的最近邻法对人脸图像进行分类,并采用留一法进行交叉验证。通过实验发现,对ORL、faces94、faces95这3个常用的人脸图像库,都能取得超过99.5%的识别正确率。     

结构化低秩字典学习的人脸识别

目的 现实中采集到的人脸图像通常受到光照、遮挡等环境因素的影响,使得同一类的人脸图像具有不同程度的差异性,不同类的人脸图像又具有不同程度的相似性,这极大地影响了人脸识别的准确性。为了解决上述问题对人脸识别造成的影响,在低秩矩阵恢复理论的基础上提出了具有识别力的结构化低秩字典学习的人脸识别算法。方法 该算法基于训练样本的标签信息将低秩正则化以及结构化稀疏同时引入到学习的具有识别力的字典上。在字典学习过程中,首先利用样本的重建误差约束样本与字典之间的关系;其次将Fisher准则应用到稀疏编码过程中,使其编码系数具有识别能力;由于训练样本中的噪声信息会影响字典的识别力,所以在低秩矩阵恢复理论的基础上将低秩正则化应用到字典学习过程中;接着,在字典学习过程中加入了结构化稀疏使其不丢失结构信息以保证对样本进行最优分类;最后再利用误差重构法对测试样本进行分类识别。结果 本文算法在AR以及ORL人脸数据库上分别进行了实验仿真。在AR人脸数据库中,为了分析样本不同维数对实验结果造成的影响,选取了第一时期拍摄的每人6幅图像,包括1幅围巾遮挡,2幅墨镜遮挡以及3幅脸部表情变化以及光照变化（未被遮挡）的图像作为训练样本,同时选取相同组合的样本图像作为测试样本,无论哪种方法,图像的维度越高识别率越高。对比SRC （sparse representation based on classification）算法与DKSVD （discriminative K-means singular value decomposition）算法的识别率可知,DKSVD算法通过字典学习减缓了训练样本中的不确定因素对识别结果的影响;对比DLRD_SR （discriminative low-rank dictionary learning for sparse representation）算法与FDDL （Fisher discriminative dictionary learning）算法的识别率可知,当图像有遮挡等噪声信息存在时,字典低秩化可以提高至少5.8%的识别率;对比本文算法与DLRD_SR算法可知,在字典学习的过程中加入Fisher准则后识别率显著提高,同时理想稀疏值能保证对样本进行最优的分类。当样本图像的维度达到500维时人脸图像在有围巾、墨镜遮挡的情况下识别率可达到85.2%;其中墨镜和围巾的遮挡程度分别可以看成是人脸图像的20%和40%,为了验证本文算法在不同脸部表情变化、光照改变以及遮挡情况下的有效性,根据训练样本的具体图像组合情况进行实验。无论哪种样本图像组合,本文算法在有遮挡存在的样本识别中具有显著优势。在训练样本只包含脸部表情变化、光照变化以及墨镜遮挡图像的情况下,本文算法的识别率高于其他算法至少2.7%,在训练样本只包含脸部表情变化、光照变化以及围巾遮挡图像的情况下,本文算法的识别率高于其他算法至少3.6%,在训练样本包含脸部表情变化、光照变化、围巾遮挡以及墨镜遮挡图像的情况下,其识别率高于其他算法至少1.9%。在ORL人脸数据库中,人脸图像在无遮挡的情况下识别率达到95.2%,稍低于FDDL算法的识别率;在随机块遮挡程度达到20%时,相比较于SRC算法、DKSVD算法、FDDL算法以及DLRD_SR算法,本文算法的识别率最高;当随机块遮挡程度达到50%时,以上算法的识别率均不高,但本文算法的其识别率仍然最高。结论 本文算法在人脸图像受到遮挡等因素的影响时具有一定的鲁棒性,实验结果表明该算法在人脸识别方面具有可行性。     

可变光照条件下的人脸图像识别

对于人脸图像识别中光照变化的影响,传统的解决方法是对待识别图像进行光照补偿,先使它成为标准光照条件下的图像,然后和模板图像匹配来进行识别。为了提高在光照条件大范围变化时,人脸图像的识别率,提出了一种新的可变光照条件下的人脸图像识别方法。该方法首先利用在9个基本光照方向下分别获得的9幅图像来构成人脸光照特征空间,再通过这个光照特征空间,将图像库中的人脸图像变换成与待识别图像具有相同光照条件的图像,并将其作为模板图像;然后利用特征脸方法进行识别。实验结果表明,这种方法不仅能够有效地解决人脸识别中由于光照变化影响所造成的识别率下降的问题,而且对于光照条件大范围变化的情况,也可以得到比较高的正确识别率。