人臉識別論文：基于局部二值模式的人臉識別算法研究

1、人臉識別論文:基于局部二值模式的人臉識別算法研究【中文摘要】隨著計算機視覺和模式識別技術(shù)的飛速發(fā)展,各種基于生物特征的識別方法應(yīng)用而生,在日常生活和各種場所中發(fā)揮著巨大的作用。在眾多基于生物特征的識別方法中,自動人臉識別技術(shù)因其獨特性,表現(xiàn)出越來越重要的研究價值和廣闊的應(yīng)用前景。人臉識別技術(shù)是典型的圖像模式分析、理解與分類計算問題,經(jīng)過近幾十年的快速發(fā)展取得了巨大的進(jìn)步,在接近理想的條件下,人臉識別系統(tǒng)可以完成一定的工作。特征提取作為人臉識別的關(guān)鍵,也成為重點研究內(nèi)容及主要挑戰(zhàn)。近年來,研究者將局部二值模式(LBP方法引入到人臉特征提取中來,取得了很大的成功。但是,LBP算子本身并不完善,特別

2、是在訓(xùn)練樣本的維數(shù)高達(dá)幾千甚至上萬維的時候,其性能會急劇下降。本文對人臉識別算法中的LBP方法進(jìn)行了深入研究及改進(jìn),并詳細(xì)討論了人臉識別門禁系統(tǒng)在軍隊重要場所中應(yīng)用的可行性。主要研究工作可概括如下:1.本文通過對LBP基本原理的研究,分析了LBP原始算子的不足之處,由此提出了基于LBP的改進(jìn)方法。先計算LBP圖像,然后對其進(jìn)行分塊,將分塊后的LBP直方圖串聯(lián)起來形成一個高維的直方圖特征矢量,接著利用經(jīng)典的PCA方法降維,最后選擇分類器進(jìn)行分類識別。該方法針對不同的圖像進(jìn)行不同的分塊,使得維數(shù)的約簡更加靈活方便,提高了特征提取的準(zhǔn)確性。2.本文討論了使用3種LBP算子對圖像進(jìn)行處理的情況,并將L

3、BP圖像進(jìn)行多種分塊后在3種人臉庫中進(jìn)行實驗,在大量的實驗數(shù)據(jù)支持下,比較各種方法和各個步驟對最終識別性能的影響。3.本文對人臉識別的門禁系統(tǒng)在軍隊內(nèi)部重點場所中的應(yīng)用進(jìn)行了展望,對基層部隊門禁管理系統(tǒng)的構(gòu)成、功能等提出了自己的看法,對部隊的正規(guī)化、現(xiàn)代化建設(shè)起了一定的推動作用。本文對LBP算子及其在人臉識別中的應(yīng)用、LBP 的降維方式、LBP的分塊、PCA的降維等方面作了較深入的研究工作,并將此種方法引入到軍事場所的使用上,對于推動自動人臉識別的進(jìn)一步發(fā)展和應(yīng)用具有積極的意義?！居⑽恼緼long 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. T

13、he method is introduced in the army, which is valuable for further development of automatic face recognition.【關(guān)鍵詞】人臉識別主元分析局部二值模式特征提取分類器【英文關(guān)鍵詞】Face Recognition Principal Component Analysis Local Binary Pattern Feature Extract Classifier【目錄】基于局部二值模式的人臉識別算法研究摘要4-5ABSTRACT5-6目錄7-9第一章緒論9-15 1.1 引言9 1.2 目

14、前國內(nèi)外人臉識別系統(tǒng)現(xiàn)狀9-11 1.2.1 國外商業(yè)系統(tǒng)9-10 1.2.2 國內(nèi)商業(yè)產(chǎn)品10-11 1.3 人臉作為生物特征識別方法的優(yōu)勢11-12 1.4 本課題的研究內(nèi)容及研究意義12-13 1.5論文的組織結(jié)構(gòu)13-15第二章人臉識別中的基本問題15-25 2.1 人臉識別方法15-23 2.1.1 人臉檢測的基本算法15-18 2.1.2 人臉識別的基本算法18-21 2.1.3 常用人臉數(shù)據(jù)相關(guān)資源21-23 2.2 人臉識別系統(tǒng)若干關(guān)鍵技術(shù)23-24 2.2.1 姿態(tài)變化23 2.2.2 光照變化23 2.2.3 人臉老化23-24 2.2.4 化妝和遮擋等人為因素24 2.3 小結(jié)24-25第三章 LBP算子的基本原理及應(yīng)用25-31 3.1 LBP算子的基本原理概述25-27 3.2 LBP 統(tǒng)一模式27-28 3.3 LBP算子的特點28-29 3.4 LBP方法的應(yīng)用29-30 3.5 小結(jié)30-31第四章基于LBP的改進(jìn)型方法31-37 4.1 基于LBP改進(jìn)型方法的提出31-32 4.2 主元分析方法32-34 4.2.1 主元分析的背景32-33 4.2.2 主元分析的基本過程33-34 4.3 基于LBP改進(jìn)型方法的構(gòu)造34-37 4.3.1 訓(xùn)練樣本的求取34-35