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.     

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.     

Generalization to Novel Views: Universal,Class-based,and Model-based Processing

A major problem in object recognition is that a novel image of a given object can be different from all previously seen images. Images can vary considerably due to changes in viewing conditions such as viewing position and illumination. In this paper we distinguish between three types of recognition schemes by the level at which generalization to novel images takes place: universal, class, and model-based. The first is applicable equally to all objects, the second to a class of objects, and the third uses known properties of individual objects. We derive theoretical limitations on each of the three generalization levels. For the universal level, previous results have shown that no invariance can be obtained. Here we show that this limitation holds even when the assumptions made on the objects and the recognition functions are relaxed. We also extend the results to changes of illumination direction. For the class level, previous studies presented specific examples of classes of objects for which functions invariant to viewpoint exist. Here, we distinguish between classes that admit such invariance and classes that do not. We demonstrate that there is a tradeoff between the set of objects that can be discriminated by a given recognition function and the set of images from which the recognition function can recognize these objects. Furthermore, we demonstrate that although functions that are invariant to illumination direction do not exist at the universal level, when the objects are restricted to belong to a given class, an invariant function to illumination direction can be defined. A general conclusion of this study is that class-based processing, that has not been used extensively in the past, is often advantageous for dealing with variations due to viewpoint and illuminant changes.     

Toward a practical face recognition system: robust alignment and illumination by sparse representation

Many classic and contemporary face recognition algorithms work well on public data sets, but degrade sharply when they are used in a real recognition system. This is mostly due to the difficulty of simultaneously handling variations in illumination, image misalignment, and occlusion in the test image. We consider a scenario where the training images are well controlled and test images are only loosely controlled. We propose a conceptually simple face recognition system that achieves a high degree of robustness and stability to illumination variation, image misalignment, and partial occlusion. The system uses tools from sparse representation to align a test face image to a set of frontal training images. The region of attraction of our alignment algorithm is computed empirically for public face data sets such as Multi-PIE. We demonstrate how to capture a set of training images with enough illumination variation that they span test images taken under uncontrolled illumination. In order to evaluate how our algorithms work under practical testing conditions, we have implemented a complete face recognition system, including a projector-based training acquisition system. Our system can efficiently and effectively recognize faces under a variety of realistic conditions, using only frontal images under the proposed illuminations as training.     

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。     

基于多Inception结构的卷积神经网络人脸识别算法

人脸识别是视觉识别的一个重要领域,由于人脸识别尺度变化范围大,光照、姿态变化剧烈以及遮挡问题,导致该类非限制条件下的识别难度较大,为了解决该类问题,本文提出了一种基于Tensorflow平台的多Inception模型,通过将多个Inception结构进行串联,再通过分解卷积核的方式减少输入参数,实现了多维度同时卷积再聚合,提高了人脸识别的精度.实验结果表明,该方法在较少参数的条件下能提取出更具区分度的人脸特征,与分类损失方法及融合了其他度量学习方式的方法相比,提高了识别准确率,减少了计算时间.     

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。     

Face recognition under varying illumination

To eliminate the effects of illumination variation, the conventional approaches firstly produce a compensation-based face image under standard illumination from the input image and then match the image with the face templates in a database. This method is not inapplicable to the input image with large illumination variation. Therefore, a novel method for varying illumination conditions is proposed. Firstly, the quotient image method is improved. Then, the nine basis images of each subject are generated by the improved quotient image method. Thirdly, one new image of each subject under the same lighting conditions with an input image is synthesized by the corresponding basis images. Finally, the synthetic images and the input image are projected to PCA plane to fulfill the recognition task. The experimental results show that the proposed approach can eliminate the effects of illumination variation and have a high recognition rate in the illumination conditions with remarkable changes.     

Multiscale Fusion of Visible and Thermal IR Images for Illumination-Invariant Face Recognition

This paper describes a new software-based registration and fusion of visible and thermal infrared (IR) image data for face recognition in challenging operating environments that involve illumination variations. The combined use of visible and thermal IR imaging sensors offers a viable means for improving the performance of face recognition techniques based on a single imaging modality. Despite successes in indoor access control applications, imaging in the visible spectrum demonstrates difficulties in recognizing the faces in varying illumination conditions. Thermal IR sensors measure energy radiations from the object, which is less sensitive to illumination changes, and are even operable in darkness. However, thermal images do not provide high-resolution data. Data fusion of visible and thermal images can produce face images robust to illumination variations. However, thermal face images with eyeglasses may fail to provide useful information around the eyes since glass blocks a large portion of thermal energy. In this paper, eyeglass regions are detected using an ellipse fitting method, and replaced with eye template patterns to preserve the details useful for face recognition in the fused image. Software registration of images replaces a special-purpose imaging sensor assembly and produces co-registered image pairs at a reasonable cost for large-scale deployment. Face recognition techniques using visible, thermal IR, and data-fused visible-thermal images are compared using a commercial face recognition software (FaceIt^®) and two visible-thermal face image databases (the NIST/Equinox and the UTK-IRIS databases). The proposed multiscale data-fusion technique improved the recognition accuracy under a wide range of illumination changes. Experimental results showed that the eyeglass replacement increased the number of correct first match subjects by 85% (NIST/Equinox) and 67% (UTK-IRIS).     

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。     

基于分块相位一致性的人脸识别算法

针对基于可见光的人脸图像的识别容易受光照和表情变化的影响,人脸的表情变化仅限于局部等问题,以及图像的相位一致性特征不受图像的亮度或对比度影响的特点,提出了一种基于分块相位一致性的人脸识别算法。该算法用log-gabor滤波器对图像进行滤波,利用相位一致性模型提取相位一致性特征图像;对每幅特征图像进行分块主元分析(PCA)处理;融合所有子图像的距离信息,采用最近邻分类器进行分类识别。实验证明该方法具有更好的识别性能。     

Individual Stable Space: An Approach to Face Recognition Under Uncontrolled Conditions

There usually exist many kinds of variations in face images taken under uncontrolled conditions, such as changes of pose, illumination, expression, etc. Most previous works on face recognition (FR) focus on particular variations and usually assume the absence of others. Instead of such a “divide and conquer” strategy, this paper attempts to directly address face recognition under uncontrolled conditions. The key is the individual stable space (ISS), which only expresses personal characteristics. A neural network named ISNN is proposed to map a raw face image into the ISS. After that, three ISS-based algorithms are designed for FR under uncontrolled conditions. There are no restrictions for the images fed into these algorithms. Moreover, unlike many other FR techniques, they do not require any extra training information, such as the view angle. These advantages make them practical to implement under uncontrolled conditions. The proposed algorithms are tested on three large face databases with vast variations and achieve superior performance compared with other 12 existing FR techniques.      

Robust Hausdorff distance measure for face recognition

Face is considered to be one of the biometrics in automatic person identification. The non-intrusive nature of face recognition makes it an attractive choice. For face recognition system to be practical, it should be robust to variations in illumination, pose and expression as humans recognize faces irrespective of all these variations. In this paper, an attempt to address these issues is made using a new Hausdorff distance-based measure. The proposed measure represent the gray values of pixels in face images as vectors giving the neighborhood intensity distribution of the pixels. The transformation is expected to be less sensitive to illumination variations besides preserving the appearance of face embedded in the original gray image. While the existing Hausdorff distance-based measures are defined between the binary edge images of faces which contains primarily structural information, the proposed measure gives the dissimilarity between the appearance of faces. An efficient method to compute the proposed measure is presented. The performance of the method on bench mark face databases shows that it is robust to considerable variations in pose, expression and illumination. Comparison with some of the existing Hausdorff distance-based methods shows that the proposed method performs better in many cases.     

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.     

基于不同视角的人脸模型识别方法

对在不同视角下,得到的人脸模型,文中提出一种基于人脸表面的识别方法,采用平面射影变换,将人脸的图像变换到一个相同的图像,使图像对齐;而后运用主成分分析法(PCA)进行分类。基于这种方法,由光线、面部表情、姿态的改变引起的不必要变化就可能被消除或可以忽略。这种方法可以达到比较准确的识别人脸的目的。实验结果显示,文中的方法对人脸模型提供了更好的表达,并且人脸识别的错误率更低。     

Robust face recognition using generalized neural reflectance model

A generalized neural reflectance (GNR) model for enhancing face recognition under variations in illumination and posture is presented in this paper. Our work is based on training a number of synthesis images of each face taken at single lighting direction with frontal/posture view. This way of synthesizing images can be used to build training cases for each face under different known illumination conditions from which face recognition can be significantly improved. However, reconstructing face shape may not easily be achieved and the human face images usually form by highly complex structure which suffers from strong specular and unknown reflective conditions. In this paper, these limitations are addressed by Cho and Chow (IEEE Trans Neural Netw 12(5):1204–1214, 2002). Face surfaces are recovered by this GNR model and face images in different poses are synthesized to create a database for training. Our training algorithm assigns to recognize the face identity by similarity measure on face features extracting first by the principle component analysis (PCA) method and then further processing by the Fisher’s discrimination analysis (FDA) to acquire lower dimensional patterns. Experimental results conducted on the Yale Face Database B show that lower error rates of classification and recognition are achieved under different variations in lighting and pose and the performance significantly outperforms the recognition without using the proposed GNR model.     

Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics

In this paper, we propose two novel methods for face recognition under arbitrary unknown lighting by using spherical harmonics illumination representation, which require only one training image per subject and no 3D shape information. Our methods are based on the result which demonstrated that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace. We provide two methods to estimate the spherical harmonic basis images spanning this space from just one image. Our first method builds the statistical model based on a collection of 2D basis images. We demonstrate that, by using the learned statistics, we can estimate the spherical harmonic basis images from just one image taken under arbitrary illumination conditions if there is no pose variation. Compared to the first method, the second method builds the statistical models directly in 3D spaces by combining the spherical harmonic illumination representation and a 3D morphable model of human faces to recover basis images from images across both poses and illuminations. After estimating the basis images, we use the same recognition scheme for both methods: we recognize the face for which there exists a weighted combination of basis images that is the closest to the test face image. We provide a series of experiments that achieve high recognition rates, under a wide range of illumination conditions, including multiple sources of illumination. Our methods achieve comparable levels of accuracy with methods that have much more onerous training data requirements. Comparison of the two methods is also provided.     

一种人脸标准光照图像的线性重构方法

基于相同光照下不同人脸图像与其标准光照图像之间的稳定关系,文中提出一种人脸标准光照图像重构方法。首先,为消除人脸结构影响,引入人脸三维变形,实现图像像素级对齐。其次,根据图像明暗变化,给出一种基于图像分块的光照分类方法。最后,对于形状对齐后的不同光照类别样本,训练出基于子空间的线性重构模型。该方法有效避免传统预处理方法带来的重构图像纹理丢失和子空间方法引起的图像失真。Extended Yale B数据库上实验表明,该方法对图像真实度与人脸识别率的提升,也验证文中人脸对齐和光照分类方法的有效性。     

基于特征运动的表情人脸识别

人脸像的面部表情识别一直是人脸识别的一个难点，为了提高表情人脸识别的鲁棒性，提出了一种基于特征运动的人脸识别方法，该方法首先利用块匹配的方法来确定表情人脸和无表情人脸之间的运动向量，然后利用主成分分析方法（PCA）从这些运动向量中，产生低维子空间，称之为特征运动空间，测试时，先将测试人脸与无表情人脸之间的运动向量投影到特征运动空间，再根据这个运动向量在特征运动空间里的残差进行人脸识别，同时还介绍了基于特征运动的个人模型方法和公共模型方法，实验结果证明，该新算法在表情人脸的识别上，优于特征脸方法，有非常高的识别率。