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.     

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.     

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。     

Face recognition across illumination

Illumination variation on images of faces is one of the most difficult problems in face recognition systems. The performance of a self-organizing map-based face recognition system is highly degraded when the illumination in test images differs from that of the training images. Illumination normalization is a way to solve this problem. Both global and local image enhancement methods are studied in this article. A local histogram equalization method strongly improves the recognition accuracy of the CMU-PIE face database. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

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.     

一种基于小波的人脸图像光照归一化算法

光照变化严重影响了人脸图像的外貌,这阻碍了人脸识别的过程。为了识别可变光照下的人脸图像,提出了一种基于小波的光照归一化算法,将一幅图像分解为低频成分和高频成分,对不同的频带成分进行不同的操作——对低频成分进行直方图均衡化,同时着重强调细节（高频成分）,将它乘以一个标量从而增强图像边缘;对修改后的系数进行逆小波变换就得到归一化后的图像。最后,直接使用PCA方法对单训练样本条件下的人脸进行识别,在AR和FERRET人脸库上的实验结果表明,提出的方法可以显著提高人脸识别系统的识别率。     

Multi-subregion based correlation filter bank for robust face recognition

In this paper, we propose an effective feature extraction algorithm, called Multi-Subregion based Correlation Filter Bank (MS-CFB), for robust face recognition. MS-CFB combines the benefits of global-based and local-based feature extraction algorithms, where multiple correlation filters corresponding to different face subregions are jointly designed to optimize the overall correlation outputs. Furthermore, we reduce the computational complexity of MS-CFB by designing the correlation filter bank in the spatial domain and improve its generalization capability by capitalizing on the unconstrained form during the filter bank design process. MS-CFB not only takes the differences among face subregions into account, but also effectively exploits the discriminative information in face subregions. Experimental results on various public face databases demonstrate that the proposed algorithm provides a better feature representation for classification and achieves higher recognition rates compared with several state-of-the-art algorithms.     

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.     

同态滤波在人脸识别中的应用研究

论文首先对人脸识别进行了介绍,通过对人脸识别系统的分析指出预处理在人脸识别中的重要性。然后对已有的人脸识别预处理法作了详细的介绍,并对各种方法进行比较。最后针对光照对人脸图像的影响还提出了同态滤波的方法,在预处理阶段消除光照对人脸图像的影响,实验结果表明,此种方法可以提高识别率。     

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.     

一种基于同态滤波器与Radon变换的光照不变人脸识别方法

为了减轻光照变化对人脸识别精度的影响,提出了一种结合同态滤波器与Radon变换的人脸识别方法。首先用高斯同态滤波器对人脸图像进行处理,然后进行Radon变换,将变换得到的Radon特征用2DPCA/2DLDA进行降维。在Yale和PIE人脸数据库上的实验结果表明,提出的方法在光照变化较大时,其识别精度远高于2DPCA和2DLDA,是一种高精度的人脸识别算法。     

Face recognition under varying illumination using Mahalanobis self-organizing map

We present an appearance-based method for face recognition and evaluate its robustness against illumination changes. Self-organizing map (SOM) is utilized to transform the high dimensional face image into low dimensional topological space. However, the original learning algorithm of SOM uses Euclidean distance to measure similarity between input and codebook images, which is very sensitive to illumination changes. In this paper, we present Mahalanobis SOM, which uses Mahalanobis distance instead of the original Euclidean distance. The effectiveness of the proposed method is demonstrated by conducting some experiments on Yale B and CMU-PIE face databases. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

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 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.     

Efficient 3D reconstruction for face recognition

Face recognition with variant pose, illumination and expression (PIE) is a challenging problem. In this paper, we propose an analysis-by-synthesis framework for face recognition with variant PIE. First, an efficient two-dimensional (2D)-to-three-dimensional (3D) integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination. Then, realistic virtual faces with different PIE are synthesized based on the personalized 3D face to characterize the face subspace. Finally, face recognition is conducted based on these representative virtual faces. Compared with other related work, this framework has following advantages: (1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; (2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex PIE; and (3) compared with other 3D reconstruction approaches, our proposed 2D-to-3D integrated face reconstruction approach is fully automatic and more efficient. The extensive experimental results show that the synthesized virtual faces significantly improve the accuracy of face recognition with changing PIE.     

Median MSD-based method for face recognition

An improved maximum scatter difference (MSD) criterion is proposed in this paper. A weakness of existing MSD model is that the class mean vector in the expressions of within-class scatter matrix and between-class scatter matrix is estimated by class sample average. Under the non-ideal conditions such as variations of expression, illumination, pose, and so on, there will be some outliers in the sample set, so the class sample average is not sufficient to provide an accurate estimate of the class mean using a few of given samples. As a result, the recognition performance of traditional MSD model will decrease. To address this problem, also to render MSD model rather robust, within-class median vector rather than within-class mean vector is used in the original MSD method. The results of experiments conducted on CAS-PEAL and FERET face database indicate the effectiveness of the proposed approach.     

相关滤波器和2DPCA相融合的变光照人脸识别

为解决变光照下人脸识别的识别率低问题,提出一种最佳相关滤波和2DPCA相融合的光照人脸识别方法。通过采用特定类2DPCA重构人脸图像,生成一对相关滤波器;测试人脸图像通过相关性滤器将投影到二维子空间中,并根据预先设定的峰旁瓣比阈值进行人脸识别;最后采用PIE和YaleB人脸库进行仿真实验。相比其他人脸识别方法,该方法获得了更高的人脸识别率,鲁棒性更强。