【作者】 付元元；

【导师】 任东；

【作者基本信息】 三峡大学 ， 计算机应用技术， 2011， 硕士

【摘要】 人脸识别研究就是要赋予计算机根据人脸面孔识别人物身份的能力,该研究具有重要的理论价值和巨大的应用前景。经过四十多年的发展,人脸识别研究已经取得了重大进展,但仍存在一些不尽人意的地方,还需要解决光照变化、姿态变化、表情变化等问题。本文主要关注于人脸识别中的光照问题,分析了光照变化影响人脸识别的原因,概述了人脸识别中光照问题的研究现状,针对目前解决方法中的一些缺点,从图像预处理、提取光照不敏感特征、图像预处理方法与光照不敏感特征结合三个方面对光照问题进行了研究,主要做了如下工作:1)实验对比分析了分块直方图匹配、分块直方图均衡化和对比度受限直方图均衡化这三种处理效果较好的基于传统图像处理技术的光照预处理方法,结果表明当人脸图像存在阴影时,这三种方法对阴影的削弱能力有限,并且在处理中增强了噪声。2)基于Retinex理论的光照正则化方法削弱阴影的能力总体上比基于传统图像处理技术的人脸预处理方法好,但这类方法在人脸图像存在较强阴影时,易在阴影边缘处产生光晕现象,光晕现象的产生严重的影响了后续的人脸识别。针对这一问题提出了基于L2范数约束的总变分模型(TVL2)的对数域光照正则化方法LogTVL2,由于TVL2具有较好的边缘保持能力,所提出的LogTVL2方法能较好的削弱光晕现象。从处理效果、处理耗时和识别率三个方面,将LogTVL2与MSR、LogDCT和SQI这三种典型方法进行了实验比较,验证了LogTVL2光照正则化方法的有效性。3)现有光照预处理方法均没有考虑输入人脸图像的光照情况,而是对它们进行相同的处理。当人脸图像存在较大的光照变化时,现有的光照预处理方法均能在一定程度上削弱光照的变化,提高识别率,但是对于光照比较均匀的人脸图像,这些处理方法却带来了负面影响。针对这一问题,提出了“人脸图像光照质量指数”,人脸图像光照质量指数定义为待测人脸图像的光照估计图像与参考人脸图像的光照估计图像的相似度,参考人脸图像的光照条件是均匀或近似均匀的,通过保留人脸图像离散余弦变换的部分低频系数估计人脸的光照图像。实验结果表明,该方法能较好的定性衡量人脸图像光照不均匀程度。根据输入人脸图像的光照质量指数可以判断该图像是否需要经过预处理,避免预处理方法对光照情况较好的人脸图像带来负面效应。4)人脸面部对象的轮廓是进行人脸识别的重要特征,并且它对光照具有一定的不敏感性,梯度是刻画这些轮廓的一种重要方式,基于Retinex理论和图像处理的角度,分析比较了对数域梯度、对数域垂直梯度及对数域水平梯度的光照不敏感性,并提出在决策级将三者加权融合,优势互补,提高对光照变化的不敏感性,并通过实验对该方法的有效性进行了验证。5)结合前面提出的方法,提出了健壮的链式处理方法,首先计算输入人脸图像的光照质量指数,根据计算值确定输入人脸图像是否需要进行光照预处理,若需要,则利用提出的LogTVL2进行光照正则化处理,然后提取梯度方向、梯度幅度特征,然后将梯度方向与梯度幅度特征在决策级别融合,进行识别;若不需要,则直接利用正则化相关进行识别。实验表明LogTVL2光照正则化方法与梯度方向的结合,能较好的提高识别性能,链式处理增强了对光照变化的鲁棒性。更多还原

【Abstract】 The target of research on face recognition is to endow computer with the ability of identifying people according faces. Face recognition has made great progress after more than forty years of research, but there still exist many unsatisfactory aspects. It needs more research on the problems of illumination variations, pose variations, expression variations and so on.The dissertation mainly focuses on illumination variations problem in face recognition. We analyze why illumination variations could affect face recognition. An overview of the state-of-the-art of illumination problem in face recognition is given. Research is conducted from three aspects: image preprocessing, extract illumination insensitive features, the combination of image preprocessing and illumination insensitive features. The main work of this dissertation is as follows:1) Block histogram equalization, block histogram match, and contrast limited adaptive histogram equalization are three illumination preprocessing methods based on traditional image processing techniques. Their performances are better than other methods of the same type. Experiments are conducted in order to compare them. The results show that they do not work well and enhance noise when there are shadows in face image.2) Illumination normalization methods based on Retinex theory have better capability of weakening shadows than illumination preprocessing methods based on traditional image processing techniques. When there are heavy shadows in face image, halo effect is produced at the edges of shadows after preprocessing by the existing illumination normalization methods based on Retinex theory. The halo effect could worsen the successive face recognition. To address this problem, an illumination normalization method named LogTVL2 based on Total Variation Model under L2 norm constraint is proposed. Because the TVL2 model has better capability of edge-preserving, the LogTVL2 could weaken halo effect well. In order to validate the performance of LogTVL2, experiments are conducted from three aspects: capability of weakening halo effect, time-consuming and performance of recognition. The LogTVL2 is compared with three representative methods MSR, LogDCT and SQI.3) At present, all the illumination preprocessing methods treat all face images in the same way without considering the specific illumination conditions. Although most of illumination preprocessing approaches can improve the recognition rate when probe images with large variations in lighting, they may have a bad effect on well-lit face images and lead to a decrease in recognition rate. To address this problem, illumination quality index of face image is proposed. The luminance images of probe face image and reference face image are first estimated. The reference face image is with normal illumination. The similarity score of the two estimated luminance images are calculated by normalized correlation. The similarity score obtained is defined as the illumination quality index of the probe image. Luminance image is estimated by only saving some low-frequency discrete cosine transform coefficients and then conducting inverse transform. Experimental results show that the illumination quality index of face image can measure the degree of uneven illumination of face image well. According to the IQI of input face image, we can decide whether illumination preprocessing should be used on it. This could avoid bringing bad effect on face images with good illumination conditions.4) The edges of facial objects are important cue for face recognition and are less sensitive to illumination changes. The illumination insensitivity of logarithmic gradient (LG), logarithmic vertical gradient (LVG) and logarithmic horizontal gradient (LHG) are compared based on Retinex theory and image processing techniques. By taking advantages of the LVG, LG and LHG, we fuse them at decision level. Experimental results demonstrate the effectiveness of the proposed method.5) Combining with the methods proposed above, a robust processing chain is proposed. Firstly the illumination quality index of probe image is calculated. According to the IQI, probe image with approximately normal illumination can be excluded. The excluded probe image needs no illumination normalization and directly step into recognition by normalized correlation. It not only decreases the possibility of misclassification but also saves times. In order to enhance robustness of face recognition to illumination variations, LogTVL2 is conducted on these non-excluded probe images. Then illumination normalized face images are fed to a new fusion algorithm combining gradient direction and magnitude at decision level. This preprocessing chain makes face recognition more reliable under varying illumination.更多还原