一种适用于人脸检测的自适应光照补偿方法

为了解决复杂光照条件下的人脸检测问题,提出一种人脸光照补偿新方法。该方法先使用高通滤波增强边缘信息,同时利用对数变换和指数变换调节全局亮度,最后利用非线性变化削弱局部高光和阴影的影响,改善图像光照不均衡的情况,最终实现光照补偿。在YaleB人脸库、Orl人脸库以及自建人脸库上分别对光照不均匀人脸图像和均匀光照下的人脸图像进行了实验,证明该方法能有效地进行光照补偿,提高人脸检测率。     

基于有效光照估计的复杂光照人脸识别

基于光照估计的光照不变量提取是提高复杂光照人脸识别性能的一种有效方法。以往算法仅考虑光照缓慢变化特性从人脸图像中估计光照,无法获取准确的光照和光照不变量。综合考虑图像的成像原理、光照缓慢变化特性和复杂照明环境,结合图像融合和平滑滤波,提出一种有效的人脸图像光照估计、光照不变量提取方法。所提算法能较好地处理阴影边缘问题,提取含有丰富面部细节特征、更接近于人脸本征的光照不变量。复杂光照Yale B+和CAS-PEAL-R1人脸库上的实验结果表明所提算法具有高效性。     

基于几何模型的单幅人脸图像光照补偿方法

为减少光照对人脸识别的影响,本文提出了一种以补偿角度和（Sum of Compensated Angle）为不变量的光照补偿新方法. 首先,补偿角度和是临界补偿状态下两幅图像的光照角度之和. 对某单光源系统,该不变量仅由光照系统决定且为定值. 其次,根据人类头骨在法兰克福截面的形状特性,我们提出了包含人头骨结构的几何人脸光照模型. 据此模型,补偿角度由不变量和待补偿图像的光照角度计算得出,从而将光照补偿转化为简单加法操作. 最后,在Yale B人脸数据库上的补偿结果表明了算法的有效性. 较Sang-Ⅱ Choi的方法显著地提高了大角度下的补偿效果,且在水平和竖直方向上更加鲁棒.     

基于局部定性表达的光照不变人脸识别方法

提出一种局部定性特征表达方法称为局部序映射(LOM)作为光照不变表达用于人脸识别。该特征表达像素在其邻域中所处的序阶位置,反应了图像像素在局部区域内的相对关系,因而对光照引起的图像大面积灰度变化有很好的不敏感性。同时提出一种针对序定性特征的逻辑距离度量,用于特征空间中最近邻分类器的相似性度量准则。 在Extended YaleB 光照变化人脸测试库中的实验表明了本文局部序映射表达的光照鲁棒性及逻辑距离度量的有效性。     

一种利用人脸对称性的光照归一化方法

光照归一化在光照鲁棒的人脸识别中被广泛使用.许多现有光照归一化方法将人脸图像视为自然图像,而忽略了人脸这一类特定物体的先验属性,因此很难从一幅具有侧光的人脸图像中恢复阴影区域中的人脸信息.提出了利用人脸对称性先验的光照归一化方法,在能量最小化框架下,对人脸图像的阴影区域进行光照归一化时参考其对称非阴影区域中的人脸结构信息,同时提出了无阴影信度图将二元最优化问题简化为一元最优化问题,以降低光照归一化方法的计算代价.在合成阴影和真实阴影人脸图像上的实验表明,利用人脸对称性的光照归一化方法能有效恢复图像阴影区域中的人脸特征,并对人脸误配准和非对称几何归一化具有一定的鲁棒性.     

一种光照不变人脸识别的预处理算法

提出了一种新的光照不变人脸识别的图像预处理算法称为分段局部归一化方法(SLN)。其思想是对图像像素分段,使得每段中各像素对应的物体表面点具有相近的表面法向量分布,因而对光源具有相似的灰度响应,然后局部归一化在各段中进行以削弱光照影响。该算法首先建立物体的朗伯(Lambert)表面反射模型,用奇异值分解方法估计出人脸形状的平均表面法向量分布矩阵,根据法向量方向利用聚类算法对像素进行分段,然后在各段中进行局部的像素归一化处理,最后传统的人脸识别算法如PCA在归一化后的图像中进行。在Harvard和YaleB人脸图像库中的识别试验表明,该算法能有效地提高在非均匀光照条件下的人脸识别率。     

未知光源参数的人脸光照恢复方法

数字人像技术在实际应用中有广泛的应用前景．在未知光源位置的非均匀光照射下的人脸光照归一化问题是人脸识别技术的一大难题．对在未知光源位置的非均匀光照射下的正面人脸图像提出了一种基于参考人脸模型的方法．它使用参考人脸模型估计人脸的光照阴影区域,把人脸的纹理表示为训练图像模型的线性组合,根据人脸光照明亮区域的纹理信息,使用最小二乘法估计出最优的线性组合系数,从而重建原图像人脸纹理实现人脸光照恢复．实验表明这种方法对于阴影处的人脸信息恢复不仅是有效的而且是快速的．     

复杂光照条件下的人脸识别方法

在人脸识别增加真实性的研究中,为了提高在光照条件变化时人脸图像的识别率,并加快运行速度,提出了一种基于小波变换域的光照处理与识别方法.由于光照对低频信息的影响较小,且低频信息在人脸识别中起到最主要作用,通过对人脸的低频逼近图像进行光照处理,采用局部二元模式来表征光照处理后的低频图像,将得到的局部二元模式特征作为人脸的鉴别特征用于分类与识别.根据YaleB、Extended YaleB人脸库的实验结果表明,在复杂的光照条件下识别率高达96%,与传统方法相比,取得了更好的识别结果.     

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.     

自适应同态对数光照补偿

人脸检测有广泛的应用前景,但目前的许多人脸检测算法对光照有一定的要求,过亮过暗偏光图像使检测率急剧下降。提出一种自适应同态对数光照补偿方法,用对数函数作为基函数,使变换后的图像符合人的视觉特点;设置对数函数的平移系数,使变换对各种程度过暗过亮偏光阴影图像都可以进行有效的补偿;引入同态补偿公式来补偿光照的同时增强图像细节,使变换后的图像更清晰。实验结果表明,该方法对过暗过亮的人脸图像都可以进行有效的光照补偿,有助于提高人脸检测率。     

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.     

基于自商图像的人脸图像增强

为了降低光照对人脸识别效果的影响,通过多层次自商图像的方法获得反射系数。将原图作为输入图像采用反锐化掩模滤波的方式进行增强,再通过自商图像方法获得反射系数和光照成分;把光照成分作为输入图像,重复以上操作,对每次得到的反射系数按比例融合得到最终的反射系数。YaleB人脸库上的实验结果表明,该方法能在一定程度上去除光照的影响,使人脸识别率有一定的提高。     

Shadow compensation and illumination normalization of face image

This study proposes a novel shadow compensation and illumination normalization method under uncontrolled light conditions. First, we decompose the face image into two images based on the Lambertian theory, which corresponds to the large- and small-scale features, respectively. Then, the threshold minimum-and-maximum filter on the small-scale features to smooth the shadow edge is applied. After that, the robust Principal Component Analysis and some normalization methods are used to remove the shadow and normalize the face image on the large-scale features. In the end, the normalized face image is obtained by combining both results from the large- and small-scale features. Our main contribution is that a more reliable shadow compensation approach is found, which can get a better normalized face image. Experiments on the Extended Yale B, CMU-PIE and FRGC 2.0 (Face Recognition Grand Challenge) face datasets show that not only the recognition performance is significantly improved, but also much better visual quality is achieved.     

融合阴影图和深度划分阴影体的阴影渲染算法

阴影图算法可以简单、快速地渲染硬阴影,但该算法渲染的硬阴影会在边缘区域出现锯齿状走样。受此影响,基于阴影图算法渲染的柔和阴影,在小尺寸半影区域依然可能会出现锯齿状走样。因此,要渲染无走样的柔和阴影,需要精确计算阴影边缘区域的着色点对点光源的可见性。深度划分阴影体算法可以精确地计算着色点对点光源的可见性,但其不仅在效率上不及阴影图算法,还无法实现柔和阴影渲染。针对上述问题,提出一种融合阴影图和深度划分阴影体的阴影渲染算法,对处于阴影边缘区域的着色点,使用深度划分阴影体算法精确计算该着色点对点光源的可见性;对其他着色点,使用阴影图算法快速计算该着色点对点光源的可见性。最后,将着色点的可见性值存储在可见性图中并滤波即可实现无走样柔和阴影的渲染。     

A method of illumination compensation for human face image based on quotient image

Illumination variation is one of the critical factors affecting face recognition rate. A novel approach for human face illumination compensation is presented in this paper. It constructs the nine-dimension face illumination subspace based on quotient image. In addition, with the aim to improve algorithm efficiency, a half-face illumination image is proposed and the low-dimension training set of the face image under different illumination conditions are obtained by means of PCA and wavelet transform. After processing, two different illumination compensation strategies are given: one is adding light, and the other is removing light. Based on the illumination compensation strategy, we implement the typical illumination sample image synthesis and the standard illumination sample image synthesis on a PCA feature subspace and a wavelet transform subspace, respectively, and the illumination compensation of the gray images and the color images are further realized. Experimental results based on the Yale Face Database B, the Extended Yale Face Database B and the CAS-PEAL Face Database indicate that execution time after compensation is approximately half the time and face recognition rate is improved by 20% compared with that of the original images.     

基于样例学习的任意光照下的人脸3D重建方法

待匹配的人脸图像与数据库中的原型图像之间的光照差异是自动人脸识别的主要瓶颈问题之一。提出了一种基于样例学习方式的3D人脸形状重建方法,既可以生成任意光照条件下的数据库中人脸图像,也可以对待识别图像进行重新光照,合成无阴影的图像。该方法在建立人脸数据库时利用光度立体技术分离人脸图像的纹理和形状信息,并用多面体模型在最小二乘意义下恢复其3D信息并更新法向量场以克服阴影误差,从而可以利用计算机图形学的方法合成任意光照条件下和小角度姿态改变时的人脸图像;在识别时采用数据库中3D数据的线性组合形式对输入图像建模,以估计其3D信息,从而可以重新照明。在YaleB人脸数据库上的实验表明,在建立3D人脸数据库后,该方法可以快速恢复输入单幅图像中人脸的3D信息,并生成任意光照条件的该人脸图像。     

Light Space Cascaded Shadow Maps Algorithm for Real Time Rendering

Owing to its generality and efficiency.Cascaded Shadow Maps(CSMs) has an important role in real-time shadow rendering in large scale and complex virtual environments.However,CSMs suffers from redundant rendering problem—objects are rendered undesirably to different shadow map textures when view direction and light direction are not perpendicular.In this paper,we present a light space cascaded shadow maps algorithm.The algorithm splits a scene into non-intersecting layers in light space,and generates one shadow map for each layer through irregular frustum clipping and scene organization,ensuring that any shadow sample point never appears in multiple shadow maps.A succinct shadow determination method is given to choose the optimal shadow map when rendering scenes.We also combine the algorithm with stable cascaded shadow maps and soft shadow algorithm to avoid shadow flicking and produce soft shadows.The results show that the algorithm effectively improves the efficiency and shadow quality of CSMs by avoiding redundant rendering. and can produce high-quality shadow rendering in large scale dynamic environments with real-time performance.     

Bitmask Soft Shadows

Recently, several real-time soft shadow algorithms have been introduced which all compute a single shadow map and use its texels to obtain a discrete scene representation. The resulting micropatches are backprojected onto the light source and the light areas occluded by them get accumulated to estimate overall light occlusion. This approach ignores patch overlaps, however, which can lead to objectionable artifacts. In this paper, we propose to determine the visibility of the light source with a bit field where each bit tracks the visibility of a sample point on the light source. This approach not only avoids overlapping-related artifacts but offers a solution to the important occluder fusion problem. Hence, it also becomes possible to correctly incorporate information from multiple depth maps. In addition, a new interpretation of the shadow map data is suggested which often provides superior visual results. Finally, we show how the search area for potential occluders can be reduced substantially.