重庆邮电大学毕业设计论文

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重庆邮电大学

毕业设计（论文）

设计（论文）题目：改进LBP的人脸识别算法研究

学院名称：软件工程学院

学生姓名：肖霖生

专业：软件工程

班级：1301106

学号：19

指导教师：周丽芳

答辩组负责人：

填表时间：2015年5 月

重庆邮电大学教务处制

摘要

在各种生物特征识别方法中，自动人脸识别有其自身特殊的优势，因而在生物识别中有着重要的地位。经过三十多年的发展，人脸识别技术取得了长足的进步，目前最好的人脸识别系统在理想情况下已经能够取得可以接受的识别性能，并已经出现了若干人脸识别系统。但由于人脸识别问题的复杂性和客观条件的多重影响，人脸识别应用系统仍然面临着许多需要解决的关键问题。人脸特征提取是人脸识别的关键，关系到分类识别算法的选取与识别正确率，从一定意义上讲，它关系到自动人脸识别系统的有效性。

局部二值模式(LBP全称)是一种灰度范围内的纹理描述方式，它从一种纹理局部近邻定义中衍生出来，最初是为了辅助性地度量局部图像对比度提出。近年来，研究者们成功地将其用于人脸特征描述和识别，并取得了显著的效果。然而，LBP算子本身还不够完善，因为像素值之间对比度的考虑缺失，令一些重要的纹理特征被丢失了，在这里我们提出一种名为LMCP的方法，这个方法通过预处理，减少了纹理特征的丢失，从而有效的解决LBP原算子对像素间对比度缺乏考虑的这个问题，从而完善了LBP原算子在这方面的不足。累赘的描述太多关键的没说到英文的也要改

【关键词】：人脸识别 LBP 光照正常化对比度分层

ABSTRACT

Automatic Face Recognition (AFR) holds an important position in various biometrics techniques for its superiority. With more than 30 years’development, AFR has made great achievements. The state-of-the-art AFR system can perform identification successfully under well-controlled environment, and many commercial AFR systems have appeared. However, due to the complexity and uncertainty of face recognition, there are still many key problems to be resolved for further application of AFR. Feature extraction is the crux of face recognition problem, which directly related to the selection of the classification algorithm and the accuracy of the system.

The local binary pattern (LBP) operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. It was first introduced as a complementary measure for local image contrast. Recently, the LBP has been successfully applied to face recognition as texture descriptor and excellent result has achieved. However, there are still many limitations in the basic LBP operator and the LBP-based face recognition algorithm. To resolve these problems, the dissertation is devoted to the investigation on LBP and its application in face recognition. However, LBP operator itself is not perfect, because the contrast between the pixel values considered missing, to make some important texture are lost, where we propose a method called LMCP, this method by pretreatment reduced loss of texture, so as to effectively solve the original operator of the LBP-pixel contrast between the lack of consideration of this issue, and thus improve the original LBP operator is insufficient in this regard.

【Key words】Face Recognition Local Binary Pattern

Normalization of illumination Contrast stratified