本文選題：流形學(xué)習(xí)　切入點：人臉識別　出處：《重慶理工大學(xué)》2017年碩士論文

【摘要】：人臉識別技術(shù)是一種生物特征識別技術(shù),由于其數(shù)據(jù)采集的友好性、面部的客觀性以及應(yīng)用場景的多樣性,使其已成為模式識別與深度學(xué)習(xí)方面的研究熱點。但人臉識別在具體應(yīng)用過程中會遇到各種實際問題,尤其是對人臉圖像特征提取的影響。不同的特征提取方法對于最終的識別有著舉足輕重的作用。早期人們一般是從紋理、形態(tài)、色彩等主觀方面進(jìn)行,難以提取人臉圖像中的本質(zhì)結(jié)構(gòu)信息。流形學(xué)習(xí)理論的發(fā)展為高維數(shù)據(jù)的特征提取提供了新的思路,而且相關(guān)的研究表明人臉數(shù)據(jù)更有可能分布于高維的非線性流形結(jié)構(gòu)上,因此非線性降維和流形學(xué)習(xí)理論越來越多地被人們應(yīng)用于圖像識別尤其是人臉識別中。本文以流形學(xué)習(xí)為基礎(chǔ),主要研究了局部線性嵌入(Locally Linear Embedding,LLE)算法和有監(jiān)督的局部線性嵌入(Supervised Locally Linear Embedding,SLLE)算法,針對偏離樣本整體分布的樣本點在低維重構(gòu)過程中可能映射在其它平面的不足,同時結(jié)合Kmeans++算法的優(yōu)點,提出了基于聚類的Cluster-SLLE算法;同時針對CSLLE算法引入新的參數(shù)、以及類內(nèi)距與類間距線性關(guān)系對噪聲魯棒性較差的缺點,改進(jìn)了算法中的距離相似性度量,與傳統(tǒng)算法相比,該算法在相關(guān)的人臉數(shù)據(jù)集檢驗中具有較高的識別率。本文的主要研究工作如下:1.對流形學(xué)習(xí)中基于全局保持以及局部保持的降維方法,如主成分分析、多維尺度分析、拉普拉斯特征映射等進(jìn)行了較為詳細(xì)的理論闡述,并在相關(guān)的數(shù)據(jù)集上進(jìn)行算法的對比分析,研究了各算法存在的優(yōu)勢與不足之處。2.在流形學(xué)習(xí)的基礎(chǔ)上,細(xì)致地分析了LLE算法、引用樣本類別信息的SLLE算法以及在具體應(yīng)用過程中參數(shù)的取值問題。SLLE算法利用樣本的類別標(biāo)簽進(jìn)行數(shù)據(jù)點間的相似性度量,但忽略了數(shù)據(jù)集中類別差異性較大的個體對整體數(shù)據(jù)的影響,因而提出了基于聚類的Cluster-SLLE算法,通過引入Kmeans++聚類算法標(biāo)識“奇異點”,對數(shù)據(jù)點間的距離矩陣作進(jìn)一步地改進(jìn),在Yale和ORL人臉數(shù)據(jù)集中表明了算法的可行性及泛化能力的提高。3.在SLLE及CSLLE算法中,類間數(shù)據(jù)點間距離及類內(nèi)數(shù)據(jù)點間距離的相似性度量呈線性關(guān)系,使得嵌入數(shù)據(jù)的判別和泛化能力仍被限制在一定的范圍;而且樣本中存在的噪聲會破壞樣本間的鄰域關(guān)系;另外,CSLLE算法雖一定程度提高了識別率,但也引入了新的不確定因素:新參數(shù)的取值問題,增加了算法的主觀性。針對此種情形,在原有算法的啟發(fā)下,提出了優(yōu)化類內(nèi)樣本間距離的度量的改進(jìn)算法,在減少參數(shù)個數(shù)的同時,也降低了噪聲對實驗的干擾,有助于人臉數(shù)據(jù)的低維嵌入表示。
[Abstract]:Face recognition is a biometric recognition technology. Because of its friendliness of data collection, objectivity of face and diversity of application scene, face recognition technology is a kind of biometric recognition technology. It has become a research hotspot in pattern recognition and depth learning, but face recognition will meet various practical problems in the process of application. In particular, the influence on facial image feature extraction. Different feature extraction methods play an important role in the final recognition. It is difficult to extract essential structure information from face image. The development of manifold learning theory provides a new idea for feature extraction of high-dimensional data, and related research shows that face data is more likely to be distributed on high-dimensional nonlinear manifold structure. Therefore, the theory of nonlinear reduced dimension manifold learning is more and more used in image recognition, especially in face recognition. The local Linear embedding algorithm and the supervised local linear embedding Locally Linear embedding algorithm are studied in this paper. In view of the shortcomings of the sample points deviating from the global distribution of the samples in the low-dimensional reconstruction process, they may be mapped in other planes. Combined with the advantages of Kmeans algorithm, the Cluster-SLLE algorithm based on clustering is proposed, and the distance similarity measurement in CSLLE algorithm is improved by introducing new parameters into CSLLE algorithm, and improving the distance similarity measure of CSLLE algorithm because of its poor robustness to noise due to the linear relationship between inter-class distance and inter-class spacing. Compared with the traditional algorithm, the algorithm has a higher recognition rate in the related face dataset test. The main research work of this paper is as follows: 1. The dimension reduction methods based on global and local preservation in convection learning, such as principal component analysis, are proposed. The multidimensional scale analysis, Laplace feature mapping and so on are discussed in detail, and the algorithms are compared and analyzed on the relevant data sets, and the advantages and disadvantages of each algorithm are studied. 2. On the basis of manifold learning, The LLE algorithm, the SLLE algorithm which refers to the sample category information, and the parameter selection problem in the process of application. SLLE algorithm uses the class label of the sample to measure the similarity between the data points. However, the influence of individuals whose data sets are quite different on the whole data is ignored, so the Cluster-SLLE algorithm based on clustering is proposed. By introducing Kmeans clustering algorithm to identify "singular points", the distance matrix between data points is further improved. In Yale and ORL face data sets, the feasibility of the algorithm and the improvement of generalization ability are demonstrated. 3. In the SLLE and CSLLE algorithms, the similarity measurement of the distance between data points between classes and the distance between data points within classes is linear. The ability of discriminating and generalization of embedded data is still limited to a certain extent, and the noise in the sample will destroy the neighborhood relationship between the samples. In addition, the CSLLE algorithm improves the recognition rate to a certain extent. However, a new uncertain factor is also introduced: the value problem of new parameters increases the subjectivity of the algorithm. In this case, an improved algorithm is proposed to optimize the measurement of the distance between samples in the class, which is inspired by the original algorithm. Not only the number of parameters is reduced, but also the interference of noise to experiments is reduced, which is helpful to the low dimensional embedded representation of face data.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 薄翠梅;韓曉春;易輝;李俊;;基于聚類選擇k近鄰的LLE算法及故障檢測[J];化工學(xué)報;2016年03期

2 王衛(wèi)衛(wèi);李小平;馮象初;王斯琪;;稀疏子空間聚類綜述[J];自動化學(xué)報;2015年08期

3 高晴;閆德勤;楚永賀;徐麗麗;;基于模糊聚類的LLE和SVM的人臉識別[J];微型機與應(yīng)用;2015年06期

4 趙小強;謝亞萍;;基于局部線性嵌入的半監(jiān)督仿射傳播聚類算法[J];蘭州理工大學(xué)學(xué)報;2015年01期

5 王麗榮;;局部線性嵌入優(yōu)化光譜回歸的魯棒人臉識別[J];計算機工程與應(yīng)用;2014年19期

6 厙向陽;趙元元;蔡院強;;基于鄰域參數(shù)動態(tài)變化的局部線性嵌入人臉識別[J];計算機應(yīng)用研究;2014年12期

7 沈杰;楊月全;王正群;唐擁政;王明輝;;半監(jiān)督局部線性嵌入算法在人臉識別中的應(yīng)用[J];鹽城工學(xué)院學(xué)報(自然科學(xué)版);2014年02期

8 蘇錦旗;張文宇;;基于模糊聚類的改進(jìn)LLE算法[J];計算機與現(xiàn)代化;2014年05期

9 萬源;周達(dá)麗;童恒慶;;LLE與核Fisher判別分析結(jié)合的人臉識別研究[J];武漢理工大學(xué)學(xué)報(信息與管理工程版);2013年06期

10 尹方平;;基于改進(jìn)的LLE和FSVM方法在人臉識別中的應(yīng)用[J];科學(xué)技術(shù)與工程;2012年34期

相關(guān)博士學(xué)位論文 前3條

1 許曉麗;基于聚類分析的圖像分割算法研究[D];哈爾濱工程大學(xué);2012年

2 李波;基于流形學(xué)習(xí)的特征提取方法及其應(yīng)用研究[D];中國科學(xué)技術(shù)大學(xué);2008年

3 黃啟宏;流形學(xué)習(xí)方法理論研究及圖像中應(yīng)用[D];電子科技大學(xué);2007年

相關(guān)碩士學(xué)位論文 前1條

1 魏艷濤;基于流形學(xué)習(xí)的數(shù)據(jù)降維方法研究[D];華中科技大學(xué);2008年

，

本文編號：1676534