人脸识别论文：基于局部二值模式的人脸识别算法研究

1、人脸识别论文:基于局部二值模式的人脸识别算法研究【中文摘要】随着计算机视觉和模式识别技术的飞速发展,各种基于生物特征的识别方法应用而生,在日常生活和各种场所中发挥着巨大的作用。在众多基于生物特征的识别方法中,自动人脸识别技术因其独特性,表现出越来越重要的研究价值和广阔的应用前景。人脸识别技术是典型的图像模式分析、理解与分类计算问题,经过近几十年的快速发展取得了巨大的进步,在接近理想的条件下,人脸识别系统可以完成一定的工作。特征提取作为人脸识别的关键,也成为重点研究内容及主要挑战。近年来,研究者将局部二值模式(LBP方法引入到人脸特征提取中来,取得了很大的成功。但是,LBP算子本身并不完善,特别

2、是在训练样本的维数高达几千甚至上万维的时候,其性能会急剧下降。本文对人脸识别算法中的LBP方法进行了深入研究及改进,并详细讨论了人脸识别门禁系统在军队重要场所中应用的可行性。主要研究工作可概括如下:1.本文通过对LBP基本原理的研究,分析了LBP原始算子的不足之处,由此提出了基于LBP的改进方法。先计算LBP图像,然后对其进行分块,将分块后的LBP直方图串联起来形成一个高维的直方图特征矢量,接着利用经典的PCA方法降维,最后选择分类器进行分类识别。该方法针对不同的图像进行不同的分块,使得维数的约简更加灵活方便,提高了特征提取的准确性。2.本文讨论了使用3种LBP算子对图像进行处理的情况,并将L

3、BP图像进行多种分块后在3种人脸库中进行实验,在大量的实验数据支持下,比较各种方法和各个步骤对最终识别性能的影响。3.本文对人脸识别的门禁系统在军队内部重点场所中的应用进行了展望,对基层部队门禁管理系统的构成、功能等提出了自己的看法,对部队的正规化、现代化建设起了一定的推动作用。本文对LBP算子及其在人脸识别中的应用、LBP 的降维方式、LBP的分块、PCA的降维等方面作了较深入的研究工作,并将此种方法引入到军事场所的使用上,对于推动自动人脸识别的进一步发展和应用具有积极的意义。【英文摘要】Along with the rapid development of computer vision

4、and pattern recognition technology, many recognition methods based on the biometrics have been developed. They play important roles in our real life and other fields. Automatic Face Recognition (AFR holds an important position in various biometrics techniques for its superiority, which has important

5、 theoretical research value and broad application prospects. Face recognition is a typical problem in image pattern analysis, understanding and classification compute area. In the past four decades, great achievement has been made in AFR. In close to ideal condition, face recognition system can acco

6、mplish certain work. Feature extraction is the crux of face recognition problem, which is one of the most important and challenging aspect of the study. Recently, the LBP has been successfully applied to face recognition as texture descriptorand excellent result has achieved. However, there are stil

7、l many limitations in the basic LBP operator and the LBP-based face recognition algorithm, when the dimension of samples exceeds thousands even reaches ten thousand. In this paper, the LBP features space is studied and analyzed from the texture features of images, and discuss the application possibi

8、lity of the access control system which based on face recognition in the armys important places. The main works are as follows:1. We proposed an improved algorithm after studying and analyzing the basic principle of LBP and the deficiency of original LBP operator. First LBP image has been computed a

9、nd divided into blocks. After this, the LBP histograms have been built up into a high dimension eigenvectors, then the classical PC A has been used to reduce the dimensions, finally classifier has been adopted for classification and recognition. This algorithm divides different images in different m

10、anner, so it makes dimension reduction more flexible and improves the accuracy of feature extraction.2. In this paper, we discuss three kinds of LBP operators and adopt many partition methods for LBP images, then experiment with images from three different face databases. Supported by a large number

11、 of experimental data, we compare the impact of various methods and steps on the final recognitionperformance.3. This paper discuss the application of access control system based on face recognition in the important places in the army, and provide suggestions on the structures, functions and realiza

12、tion methods of the access control systemin basic unit army, which plays an important role on normalization and modernization construction of thetroops.This paper research on LBP operator and its applicationin face recognition, LBP dimension reduction, LBP block and PCA dimension reduction deeply. The method is introduced in the army, which is valuable for further development of automatic face recognition.【关键词】人脸识别主元分析局部二值模式特征提取分类器【英文关键词】Face Recognition Principal Component Analysis Local Binary Pattern Feature Extract Classifier【目录】基于