New MCT-based face recognition under varying lighting conditions

This paper presents a new face recognition algorithm that is insensitive to variations in lighting conditions. In the proposed algorithm, the MCT (Modified Census Transform) was embedded to extract the local facial features that are invariant under illumination changes. In this study, we also employed an appearance-based method to incorporate both local and global features. First, input facial images are transformed by the MCT and a bit string from the MCT is converted to a decimal number to generate an MCT domain image. This domain image is recognized using principle component analysis (PCA) or linear discriminate analysis (LDA). Experimental results reveal that the recognition rate of the proposed approach is better than that of conventional appearance-based algorithms by approximately 20% for the Yale B database, in the case of severe variations in illumination conditions. We also found that the proposed algorithm yields better performance for the Yale database for various face expressions, eye-wear, and lighting conditions.     

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.     

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.     

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。     

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。     

消除光照和姿态变化影响的人脸识别

光照和姿态变化带来的影响是自动人脸识别的两个主要瓶颈问题。提出了消除这两方面影响的处理方法：首先对训练集里的图像应用灰度归一化处理,降低对光照强度的敏感度;然后进行姿态估计,并用特征脸方法计算不同姿态的特征子空间,最后提出了“姿态权重PWV（Pose’s Weight Value）”这一概念,据此设计了加权的最小距离分类器WMDC（Weighted Minimum Distance Classifier）,分配不同姿态权重消除姿态变化影响。在FERET和Yale B数据库上的实验结果表明,此方法能在很大程度上提高人脸光照和姿态改变时的识别率。     

结合DCT和WT的多分类器融合的光照人脸识别

为进一步提高各种光照条件下的人脸识别精度,提出了一种将光照补偿和光照不变特征提取相结合的人脸识别方法。算法先应用对数域DCT进行光照补偿;然后,用三次样条二进小波分解提取一个低频子图和三个对光照变化鲁棒的边缘细节子图;接着,用二维线性判别分析进行特征降维并构造四个分量分类器;最后,通过多分类器融合规则进行融合分类。该文算法在CAS-PEAL人脸库光照子集上的实验达到了83.91%的识别率,在YaleB人脸库上则实现了100%的识别率,实验结果证明了该文算法对光照变换具有较好的鲁棒性。     

人脸识别中基于多小波变换的光照补偿

提出了一种用于非均匀光照条件下人脸识别的光照补偿算法。该算法在对数域计算2维多小波变换来实现人脸光照补偿,然后直接在对数域进行人脸识别。在Yale B人脸库中与其它光照补偿算法进行了比较,实验结果表明,该方法的平均误识率仅为0.70%,优于现有的绝大多数算法。     

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.     

Total variation models for variable lighting face recognition

In this paper, we present the logarithmic total variation (LTV) model for face recognition under varying illumination, including natural lighting conditions, where we rarely know the strength, direction, or number of light sources. The proposed LTV model has the ability to factorize a single face image and obtain the illumination invariant facial structure, which is then used for face recognition. Our model is inspired by the SQI model but has better edge-preserving ability and simpler parameter selection. The merit of this model is that neither does it require any lighting assumption nor does it need any training. The LTV model reaches very high recognition rates in the tests using both Yale and CMU PIE face databases as well as a face database containing 765 subjects under outdoor lighting conditions.     

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.     

基于光照分类的可变光照下人脸识别方法

针对人脸识别中的光照变化问题,借鉴“分而治之”的思想,提出通过光照分类来提高不同光照情况下人脸的识别率。根据人脸图像灰度随光照变化的分布特点,将图像划分为三类：无偏光类、左偏光类和右偏光类,分别在不同的光照子集中对人脸图像进行处理与识别,并在YALEB人脸库上完成实验验证。结果表明,该方法不需要进行光照归一化处理,有效减弱了光照不均匀对人脸识别的影响,在提高识别率的同时降低了运算量,识别率可从未分类前的86.7%提高到99.6%,对于可变光照下的人脸识别有一定的应用前景。     

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.     

Gabor相位特征的人脸光照不变量提取

为了消除光照变化对人脸识别的影响,提出一种基于Gabor相位特征的光照不变量提取算法。该算法首先对图像进行光照归一化,一定程度上减弱了不同光照条件的影响;然后利用一组不同方向的2维实Gabor小波对图像进行变换,在兼顾频谱与相位信息的情况下组合变换后的Gabor系数,提取其相位特征,得到光照不变量。在Yale B和CMU PIE人脸库上的实验结果表明,该算法能够有效消除光照变化对人脸识别的影响,提取的光照不变量具有一定的鲁棒性。     

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.     

A novel efficient local illumination compensation method based on DCT in logarithm domain

In this paper, a novel illumination compensation method for face recognition under illumination variations is proposed. Rather than performing illumination compensation in a global way, the proposed method uses low-frequency discrete cosine transform (DCT) coefficients in the logarithm domain to estimate illumination in local areas. To estimate the illumination of every point more precisely, a mean operator is applied to refine the estimation. Experimental results on the CMU PIE database, the Yale Face database B and the Extended Yale Face database B demonstrate that the method is superior in comparison with other existing methods. Furthermore, a simplified version of the method is also proposed. Both theoretical analysis and experimental results demonstrate the validity and high computational efficiency of the simplified version. Performances of the proposed methods under different values of parameters are also discussed in the paper.     

基于多特征融合的复杂光照人脸识别

提取人脸图像光照不变量是提高不完备训练样本人脸识别光照鲁棒性的一个有效途径。以往算法分别从不同角度提取人脸图像的高频特征作为光照不变量不能提取完整的人脸本征,具有一定的局限性。从特征级和决策级融合的角度提出了一种基于多特征融合的复杂光照人脸识别方法。所提算法能发挥不同光照不变量的自身优势,明显提高复杂光照人脸识别的光照鲁棒性。Yale B+和非控光照人脸库的实验结果表明所提算法的有效性。     

基于Armlets的人脸光照补偿

提出了一种用于非均匀光照条件下人脸识别的光照补偿算法。该算法通过在对数域计算2维Armlets多小波变换来实现人脸光照补偿,然后直接在对数域进行人脸识别。在Yale B人脸库中与其他光照补偿算法进行了比较,实验结果表明,该方法的平均误识率仅为0.18%,优于现有的其他算法。     

CLAHE优化低频DCT系数重变换的鲁棒人脸识别

针对光照、表情、姿态、遮挡等变化显著影响人脸识别系统性能的问题,提出了基于限制对比度自适应直方图均衡化（CLAHE）的低频离散余弦变换（DCT）系数重变换算法。将图像划分成多个互不重叠的局部小块,使用CLAHE对每个局部小块进行局部对比拉伸以实现去噪,通过缩减适当数目的低频DCT系数来消除人脸图像中的光照变化;利用核主成分分析进行特征提取,采用K-最近邻分类器以完成最终的人脸识别。在ORL、扩展YaleB和AR人脸数据库上的实验验证了所提算法的有效性和鲁棒性,实验结果表明,相比其他几种较为先进的人脸识别技术,所提算法取得了更高的识别率,同时大大降低了识别所用时间。     

提取多尺度光照不变量的人脸识别

光照变化条件下的人脸图像识别一直以来都是图像处理中的热点和难点问题,为了提高人脸图像的识别率,提出了一种用于非均匀光照条件下人脸识别的算法.利用对数及二维小波变换的多尺度特性提取出人脸的光照不变量,然后运用PCA+LDA方法进行人脸特征提取,并采用基于欧氏距离的最近邻分类器进行识别.通过Matlab编程实验,在Yale B人脸库中达到了较高的识别率.