复杂光照条件下的人脸预处理算法

为了提高光照条件下的人脸识别正确率,提出一种复杂光照条件下的人脸预处理算法。对人脸图像进行局部增强处理,用双边滤波对图像亮度进行估计,采用Gamma校正补偿图像亮度估计产生的损失,将反射分量与亮度估计结果融合获得效果更优的人脸图像,并用K近邻算法建立分类器对人脸进行识别。在Yale、PIE和AR人脸库仿真结果表明,该算法提高了光照条件下的人脸识别正确率,其性能优于当前典型人脸识别算法。     

基于HMM的单样本可变光照、姿态人脸识别

提出了一种基于HMM的单样本可变光照、姿态人脸识别算法.该算法首先利用人工配准的训练集对单张正面人脸输入图像与Candide3模型进行自动配准,在配准的基础上重建特定人脸三维模型.对重建模型进行各种角度的旋转可得到姿态不同的数字人脸,然后利用球面谐波基图像调整数字人脸的光照系数可产生光照不同的数字人脸.将产生的光照、姿态不同的数字人脸同原始样本图像一起作为训练数据,为每个用户建立其独立的人脸隐马尔可夫模型.将所提算法对现有人脸库进行识别,并与基于光照补偿和姿态校正的识别方法进行比较.结果显示,该算法能有效避免光照补偿、姿态校正方法因对某些光照、姿态校正不理想而造成的识别率低的情况,能更好地适应光照、姿态不同条件下的人脸识别.     

基于支持向量机的人脸识别技术研究

研究人脸识别优化问题,不同程度光照对人脸图像的采集具有不利影响,使图像中包含一些噪声信息,而当前人脸识别算法没有考虑不同程度光照对人脸图像的影响,仅在光照变化不大时,识别正确率高.为了解决光照条件对人脸识别不利影响,提高脸识别正确率,提出一种多尺度Retinex( MSR)和支持向量机(SVM)相结合的人脸识别算法(MSR - SVM).MSR - SVM首先采用MSR对人脸图像进行预处理,消除光照变化的不利影响,然后采用PCA提取人脸图像特征,消除一些噪声信息,最后利用SVM分类算法对人脸图像进行分类.采用Yale人脸库对MSR - SVM算法进行仿真测试,仿真结果表明,改进方法可以消除光照变化对人脸识别不利影响,加快了人脸识别速度,提高了人脸识别正确率.     

改进单尺度Retinex的光照人脸识别

经典Retinex算法假设场景中光照是平缓变化的,当光照变化比较强烈时,易产生“光晕”现象,为了提高光照条件变化下的人脸识别率,提出一种改进单尺度Retinex的光照人脸识别方法。采用双曲正切函数代替Retinex的对数函数对人脸图像进行亮度和对比度非线性增强;利用双边滤波代替Retinex的高斯滤波消除“光晕”,采用Retinex消除光照不利影响,采用K近邻算法建立人脸分类器。结果表明,改进Retinex降低了时间复杂度,图像增强效果优于同类算法,提高了人脸识别率,很好地解决了“光晕”问题,具有光照鲁棒性,可适用于光照变化较强条件下的人脸识别。     

基于3D人脸重建的光照、姿态不变人脸识别

待匹配人脸图像与库存原型图像之间姿态和光照的差异是自动人脸识别的两个主要瓶颈问题,已有的解决方法往往只能单独处理二者之-,而不能同时处理光照和姿态问题.提出了一种对人脸图像中的姿态和光照变化同时进行校正处理的方法,即通过光照不变的3D人脸重建过程,将姿态和光照都校正到预先定义的标准条件下.首先,利用先验的统计变形模型,结合人脸图像上的一些关键点来恢复较为精细的人脸3D形状.基于此重建的3D形状,进而通过球面谐波商图像的方法估计输入图像的光照属性并提取输入图像的光照无关的纹理信息,从而将光照无关的3D人脸完全重构出来,生成输入人脸图像在标准姿态和光照条件下的虚拟视图,用于最终的分类识别,实现了对光照和姿态问题的同时处理.在CMU PIE数据库上的实验结果表明,此方法可以在很大程度上提高现有人脸识别方法对于原型集合(gallery)和测试集合中图像在姿态和光照不一致情况下识别结果的正确性     

基于改进BP神经网络的人脸识别算法

神经网络具有运行计算速度慢,不容易收敛的缺点,文中针对此问题提出了图像的光照校正、图像降维与改进型神经网络相结合的人脸识别算法.运用了图像进行光照校正,人脸图像进行降维及不同的光照条件下的人脸图像运用改进型的BP神经网络对进行识别.讨论了基于网络中的参数数据选择问题,对网络学习速度和Sigmoid函数进行了明显改善.实验结果表明,其识别率有了显著的提高;改进后的BP网络收敛速度在得到相同识别率的效果下显著加快.     

基于曲波变换和Retinex人脸光照处理算法

针对光照对人脸识别影响问题,提出了一种基于曲波变换和Retinex人脸光照处理的算法。该算法对光照变化人脸对数变化后的图片进行曲波变换(Curvelet);利用Kimmel变分模型作为平滑滤波算子对低频图像进行平滑滤波,对高频系数进行阈值去噪。通过曲波逆变换得到光照亮度成分图像;利用Retinex模型提取光照不变成分。通过Yale B与CMU PIE人脸库的实验结果表明:该算法能有效地消除光照变化对人脸识别的影响并提高人脸识别率。     

基于非正面人脸肤色检测的光照补偿算法的改进

非正面人脸图像比正面人脸图像更容易产生因角度原因导致光照不均匀进而肤色出现色彩偏差的问题,为了应对这种问题,详细分析了灰色世界法、完美反射法、动态阈值法和参考白算法,并在非正面人脸库进行实验.经过研究发现,可以根据平均亮度来选择光照补偿算法.实验结果表明,在平均亮度小于100时,采用参考白算法,平均亮度大于150时,采...     

基于Radon变换的人脸识别

针对目前大部分的人脸识别算法不能同时克服光照、表情、遮挡等复杂条件干扰的问题,提出一种基于Radon变换的人脸识别算法。该算法首先采用一种光照归一化算法对人脸图像进行预处理,然后采用Radon变换提取人脸的不变特征,最后采用二维最近邻分类器进行人脸识别。通过在AR人脸库的人脸识别实验,说明了该算法对多种复杂条件下的人脸识别具有一定的鲁棒性和普适性。     

光照变化条件下的人脸识别技术研究

为了提高光照变化条件下的人脸识别率,针对Retinex算法处理人脸光照图像时易产生“光晕”难题,提出了一种基于Mean-Shift滤波的Retinex算法,并应用于人脸识别中的光照预处理。对人脸图像进行非线性增强;利用Mean-Shift滤波代替高斯滤波对光照估计,解决传统Retinex算法中存在的“光晕”难题。采用Yale B人脸库对算法性能进行测试,结果表明,该算法能够很好地抑制“光晕”现象的发生,具有光照鲁棒性,提高了人脸的识别率。     

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.     

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.     

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.     

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.     

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.     

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.     

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。     

基于色度一致性的室外场景光照参数估计

针对不同天气情况下在同一太阳方位拍摄的室外场景图像,提出了一种基于色度一致性的光照参数估计算法。该算法基于太阳光与天空光基图像分解理论,利用色度一致性这一约束条件求解太阳光和天空光的光照系数;并利用光照色度校正模型对基图像进行光照色度校正,从而得到更准确的光照参数。 实验结果表明,所提算法是有效且正确的,根据基图像和光照系数可以准确重构原图像,从而实现虚拟物体与真实场景的无缝融合。     

一种快速实用的灰度校正算法

介绍了光学成像系统灰度衰减模型，提出了一种快速的灰度不均图像校正方法。该方法不需要其他的外部参数，也不需要模型，仅通过对灰度不均的图象自身进行运算来加以校正，它基于通过原图象减去背景图像来消除不均的策略，根所图象特性，直接从原图象中取出属于背景上的点，再通过插值或曲面拟合等方法得到整个背景图象。该方法不但保持了原图象上的目标，而且效果明显，简单快速，并具有很好的通用性，高速公路路面病害检测实验结果验证了该方法的有效性。     

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.