基于小波商图像的人脸光照补偿

复杂光照条件下的人脸识别是一个困难但需迫切解决的问题,为此提出了一种有效的光照补偿算法.该方法根据人脸光照线性变换子空间理论--商图像理论,构造了小波低维训练集,实现了对待识别图像的光照条件估计,并且通过加光和去光策略增强了光照补偿效果.与传统商图像理论相比,该方法利用小波分解,提高的算法执行效率,实验结果表明,该算法以较小的代价取得了较高的识别性能.     

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.     

基于线性子空间和商图像理论的人脸光照补偿

光照是影响人脸识别率的主要因素,它已成为人脸识别技术发展的瓶颈。根据商图像理论,在所构造的低维训练集上对待识别的图像进行光照条件估计,通过加光和去光两种方法,实现了光照补偿的目的,并通过识别实验验证了其补偿效果。     

Rough membership function based illumination classifier for illumination invariant face recognition

This paper proposes a face recognition system to overcome the problem due to illumination variation. The propose system first classifies the image's illumination into dark, normal or shadow and then based on the illumination type; an appropriate technique is applied for illumination normalization. Propose system ensures that there is no loss of features from the image due to a proper selection of illumination normalization technique for illumination compensation. Moreover, it also saves the processing time for illumination normalization process when an image is classified as normal. This makes the approach computationally efficient. Rough Set Theory is used to build rmf illumination classifier for illumination classification. The results obtained as high as 96% in terms of accuracy of correct classification of images as dark, normal or shadow.     

基于图像子空间的改进商图像方法

光线变化将显著降低人脸识别系统的性能。Shashua et al．提出了一种处理人脸识别中的光线变化问题的简便方法——商图像方法。在本文中，我们从图像子空间的角度对商图像方法进行了分析，理论分析和实验表明，这种方法存在的主要缺点有：1）不准确的理想类假设；2）简单的三维点光源模型无法很好地近似任意光照情况。针对这些不足，我们提出了一种基于图像PCA子空间的改进商图像方法，以克服这些缺点。我们的方法能够较好地满足商图像方法的理论前提，从而达到更好的图像合成效果和人脸识别性能。     

Shadow compensation in 2D images for face recognition

Illumination variation that occurs on face images degrades the performance of face recognition. In this paper, we propose a novel approach to handling illumination variation for face recognition. Since most human faces are similar in shape, we can find the shadow characteristics, which the illumination variation makes on the faces depending on the direction of light. By using these characteristics, we can compensate for the illumination variation on face images. The proposed method is simple and requires much less computational effort than the other methods based on 3D models, and at the same time, provides a comparable recognition rate.     

Face recognition under varying illumination based on a 2D face shape model

This paper proposes a novel illumination compensation algorithm, which can compensate for the uneven illuminations on human faces and reconstruct face images in normal lighting conditions. A simple yet effective local contrast enhancement method, namely block-based histogram equalization (BHE), is first proposed. The resulting image processed using BHE is then compared with the original face image processed using histogram equalization (HE) to estimate the category of its light source. In our scheme, we divide the light source for a human face into 65 categories. Based on the category identified, a corresponding lighting compensation model is used to reconstruct an image that will visually be under normal illumination. In order to eliminate the influence of uneven illumination while retaining the shape information about a human face, a 2D face shape model is used. Experimental results show that, with the use of principal component analysis for face recognition, the recognition rate can be improved by 53.3% to 62.6% when our proposed algorithm for lighting compensation is used.     

Robust face recognition using 2D and 3D data: Pose and illumination compensation

The paper addresses the problem of face recognition under varying pose and illumination. Robustness to appearance variations is achieved not only by using a combination of a 2D color and a 3D image of the face, but mainly by using face geometry information to cope with pose and illumination variations that inhibit the performance of 2D face recognition. A face normalization approach is proposed, which unlike state-of-the-art techniques is computationally efficient and does not require an extended training set. Experimental results on a large data set show that template-based face recognition performance is significantly benefited from the application of the proposed normalization algorithms prior to classification.     

基于照度补偿的人脸图像遮挡阴影消除处理*

为了消除人脸图像中的遮挡阴影对识别精度的影响,采用数学形态学处理对阴影进行检测分离,根据其照度损失不同划分为阴影边缘区和阴影主体区,分别实施照度补偿,并将非阴影区、阴影边缘区和阴影主体区光强光顺过渡,实现对图像阴影区域的恢复处理。基于该照度补偿原理处理实例表明,该方法可以较好地去除人脸图像中部因遮挡造成的阴影,显著改善图像质量。     

基于CSSD的非线性相关滤波在光照变化人脸识别中的应用

针对现有的滤波算法由于光照变化而影响人脸识别性能的问题,提出了特定类子空间依赖的非线性相关滤波算法。首先,利用非线性最佳映射图像相关滤波器与非线性最佳重建图像相关滤波器之间相位的特定类子空间运算实现算法;然后,通过最小化相关平面能量、同时最大化相关波峰进一步优化;最后,利用关联分类器完成人脸识别。在扩展Yale B和PIE人脸库上的实验结果表明,本文算法在加性高斯噪声条件下仍然对光照变化不敏感,相比其他几种较好的滤波算法,本文算法取得了更高的识别率,并提高了算法执行效率。     

Multiscale facial structure representation for face recognition under varying illumination

Facial structure of face image under lighting lies in multiscale space. In order to detect and eliminate illumination effect, a wavelet-based face recognition method is proposed in this paper. In this work, the effect of illuminations is effectively reduced by wavelet-based denoising techniques, and meanwhile the multiscale facial structure is generated. Among others, the proposed method has the following advantages: (1) it can be directly applied to single face image, without any prior information of 3D shape or light sources, nor many training samples; (2) due to the multiscale nature of wavelet transform, it has better edge-preserving ability in low frequency illumination fields; and (3) the parameter selection process is computationally feasible and fast. Experiments are carried out upon the Yale B and CMU PIE face databases, and the results demonstrate that the proposed method achieves satisfactory recognition rates under varying illumination conditions.     

自商图像

分析了 Retinex 及其与商图像 （Quotient Image, QI） 的关系,提出了自商图像 （self-quotient image） 的概念,以提取一个目标（如人脸）图像的内在特性,同时去除与光照相关的外在因素.SQI （self-quotient image） 方法克服了原始的 QI 方法的局限性,且能够提取与光照无关的内在特性.对算法适用的条件进行了理论分析,并在 SQI 计算过程中提出了一个非迭代的各向异性滤波器以减小光晕现象和提高计算速度.实验结果表明该方法可以有效地提高不同光照条件下的人脸识别系统的识别率.     

Face recognition based on 2D images under illumination and pose variations

We propose a novel 2D image-based approach that can simultaneously handle illumination and pose variations to enhance face recognition rate. It is much simpler, requires much less computational effort than the methods based on 3D models, and provides a comparable or better recognition rate.     

The local quotient image method-an iuumination preproassing method for face recogaition

Differences in illumination of the same face can defeat simple face recognition systems, yet most methods that compensate are too difficult to implement. Local quotient image (LQI) is an efficient illumination preprocessing method for face recognition systems. An illumination model and a face model were developed, and their use in the new method was analyzed. Analysis of the method's computational complexity showed it to be efficient. Experimental results on Yale Face Database B showed that the method can effectively eliminate the effects of differences in illumination and provides considerable improvement in recognition rates.     

Variable lighting face recognition using discrete wavelet transform

This paper presents a new discrete wavelet transform (DWT) based illumination normalization approach for face recognition under varying lighting conditions. Our method consists of three steps. Firstly, DWT-based denoising technique is employed to detect the illumination discontinuities in the detail subbands. And the detail coefficients are updated with using the obtained discontinuity information. Secondly, a smooth version of the input image is obtained by applying the inverse DWT on the updated wavelet coefficients. Finally, multi-scale reflectance model is presented to extract the illumination invariant features. The merit of the proposed method is it can preserve the illumination discontinuities when smoothing image. Thus it can reduce the halo artifacts in the normalized images. Moreover, only one parameter involved and the parameter selection process is simple and computationally fast. Experiments are carried out upon the Yale B and CMU PIE face databases, and the results demonstrate the proposed method can achieve satisfactory recognition rates under varying illumination conditions.     

基于加权边缘弱化引导滤波的人脸光照补偿

光照的变化是影响人脸识别结果的重要因素之一,针对这一问题,提出一种基于加权边缘弱化引导滤波的人脸光照补偿方法。首先为引导滤波损失函数添加一个可区分边缘细节的惩罚项,然后为惩罚项加权,加权系数由正面光照样本的类间平均脸计算得到,最后将滤波后的图像作为自商图中的平滑图,得到光照补偿图像。实验结果表明,该方法弱化了人脸平滑区域由光照造成的边缘细节噪声,且使用光照补偿图像作为人脸识别输入,能有效提高人脸识别准确率,特别在光照大范围变化时,识别准确率提升程度更高。     

HSV变换和同态滤波的彩色图像光照补偿

为了有效消除光照对彩色图像的影响,提出应用HSV变换和同态滤波的光照补偿方法。首先将图像从RGB色彩空间变换至HSV色彩空间,然后将高斯高通滤波传递函数引入同态滤波中,设计出一种新的动态高斯同态滤波器,在频域内对图像亮度分量进行增强,并保持色调和饱和度不变,在增强图像细节的同时,削减图像低频分量,弥补因光照不足引起的图像质量下降,实现对彩色图像的光照补偿。实验分析表明,所提出方法能够弱化光照影响,提高彩色图像质量,是一种比传统高斯高通滤波和Gamma矫正更有效的光照补偿方法。     

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。     

Extraction of illumination invariant facial features from a single image using nonsubsampled contourlet transform

Face recognition under varying lighting conditions is challenging, especially for single image based recognition system. Exacting illumination invariant features is an effective approach to solve this problem. However, existing methods are hard to extract both multi-scale and multi-directivity geometrical structures at the same time, which is important for capturing the intrinsic features of a face image. In this paper, we propose to utilize the logarithmic nonsubsampled contourlet transform (LNSCT) to estimate the reflectance component from a single face image and refer it as the illumination invariant feature for face recognition, where NSCT is a fully shift-invariant, multi-scale, and multi-direction transform. LNSCT can extract strong edges, weak edges, and noise from a face image using NSCT in the logarithm domain. We analyze that in the logarithm domain the low-pass subband of a face image and the low frequency part of strong edges can be regarded as the illumination effects, while the weak edges and the high frequency part of strong edges can be considered as the reflectance component. Moreover, even though a face image is polluted by noise (in particular the multiplicative noise), the reflectance component can still be well estimated and meanwhile the noise is removed. The LNSCT can be applied flexibly as neither assumption on lighting condition nor information about 3D shape is required. Experimental results show the promising performance of LNSCT for face recognition on Extended Yale B and CMU-PIE databases.