【作者】 闫凯；

【导师】 任获荣；

【作者基本信息】 西安电子科技大学 ， 测试计量技术及仪器， 2011， 硕士

【摘要】 人脸识别技术在实际中已经得到广泛的应用,而现在大多数人脸识别算法都对光照比较敏感,光照问题已经成为影响识别结果最主要的因素之一。处理光照问题最常用的一种方法是寻找具有光照不变特性的不变量来描述光照条件下的图像,这些光照不变量主要包括图像的高频和边缘信息。而提取图像高频和边缘信息的一种有效办法为TV模型。基于TV模型的光照不变量提取方法能够在保持图像边缘的基础上,充分地提取用于识别的人脸高频细节特征,但也存在对光照不变量划分不够精确以及参数优化过于随机的问题,针对以上问题引出了基于G范数的TV模型,并在此基础上提出了一种基于自适应参数的G范数TV模型,此模型可以对人脸细节特征进行更精确的划分,得到更有利于识别的光照不变量。针对TV模型存在全局化、常值区域等问题,提出了基于TV模型和Contourlet变换相结合的方法,算法充分利用了Contourlet变换局部性、多方向性和TV模型保持边缘的优点,能有效地提取用来识别的人脸光照不变量。在Yale-B人脸数据库上,本文提出的两种模型的平均识别率相对于直接使用PCA+LDA分别提高了40.11%和40.65%,在最恶劣光照条件下都提高了86.87%。相对于传统的TV模型,平均识别率也分别提高了1.91%和2.45%,在最恶劣光照条件下分别提高了1.41%和1.95%,并且基于自适应参数的G范数TV模型还有效地减少了参数优化的时间。这表明本文提出的算法能够较好地改进传统TV模型的缺点,是非常有效的光照不变量提取方法。更多还原

【Abstract】 Face Recognition Technology has been widely used in practice, but most face recognition algorithms are very sensitive to light, illumination has became one of the most decisive factors for the recognition result. One of most common method for illumination problems is looking for a illumination normalization, the illumination normalization mainly include high-frequency and edge information of image, it is insensitive to light and can describe image under different light conditions. TV model is an effective method for extracting high-frequency and edge information of image.TV model can well maintain the edge of image and extract most useful high-frequency details for face recognition, but the illumination normalization obtained by TV model based L norm is not precise, and the parameters of this TV model is very random. To solve the above problems, we lead to the TV model based on G-norm, and propose an adaptive G-norm TV model, this model can obtain more accurate illumination normalization, and it is more conductive to recognize. To solve the globalization and the constant area of TV model, we propose a new model combing TV model and Contourlet transform, This algorithm takes full advantage of localization and the multi-dimensional of Contourlet transform and the edge-preserve ability of total variation models, it can effectively obtain the face illumination normalization for the face recognition.Experiments are carried out Upon the Yale-B database demonstrate that the proposed method achieves satisfactory recognition rates under varying illumination conditions. Compared with directly method of PCA+LDA, the proposed method has an average recognition ratio increase 40.11% and 40.65%. and all increase 86.87% in the worst light conditions; Compared with the traditional TV model, has an average recognition ratio increase 1.91% and 2.45%, and respectively increase 1.41% and 1.95%, so the proposed model are effective method for face illumination normalization.更多还原