本文關(guān)鍵詞： 人臉識別 特征提取 加權(quán)稀疏重構(gòu) 稀疏子空間學(xué)習(xí) 圖嵌入　出處：《山東師范大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：人臉識別是模式識別領(lǐng)域中的一個涉及面非常廣的重要研究方向。由于人臉圖像在采集時受環(huán)境、光照、表情和姿態(tài)等多種變化的影響,使得人臉識別研究極富挑戰(zhàn)性。如何快速準(zhǔn)確地利用計算機(jī)進(jìn)行人臉的檢測與識別是當(dāng)前人臉識別技術(shù)的關(guān)鍵所在。目前,盡管人臉識別研究已經(jīng)取得了一些成績,但是仍有許多問題和關(guān)鍵技術(shù)有待進(jìn)一步解決和完善,其中主要包括:人臉特征提取的充分性研究,即如何充分嵌入局部和全局結(jié)構(gòu)信息等;人臉特征識別的分類性能研究,即設(shè)計具有高精度識別率和快速分類的算法等�；谙∈璞硎镜娜四樧R別技術(shù),具有簡單的理論基礎(chǔ)和較好的魯棒性。因此本文對稀疏表示人臉識別算法進(jìn)行了研究,研究重點(diǎn)在特征提取和分類識別上,提出了一些新的人臉特征提取和識別算法。通過在人臉基準(zhǔn)數(shù)據(jù)庫上進(jìn)行的大量實(shí)驗(yàn),表明本文算法在人臉識別的計算效率和識別率上獲得了良好效果。本論文的主要工作和貢獻(xiàn)如下:(1)提出一種加權(quán)主成分分析特征提取算法。新算法首先通過線性擬合標(biāo)記信息與特征維來對各特征加權(quán),并通過稀疏約束使部分特征的權(quán)值為零,然后進(jìn)行主成分分析特征提取。該方法實(shí)現(xiàn)了特征預(yù)選擇并且突出了重要特征屬性。實(shí)驗(yàn)結(jié)果表明,新算法不僅能夠降低計算復(fù)雜度,還能提高分類的精度。(2)提出兩種稀疏保持投影特征提取算法。一種是加權(quán)稀疏鄰域保持投影,使用一個加權(quán)的稀疏重構(gòu)模型去學(xué)習(xí)重構(gòu)系數(shù),并通過限制非零重構(gòu)系數(shù)的個數(shù),降低了時間復(fù)雜度,提高了識別精度和全局魯棒性。另一種是基于聚類的無監(jiān)督判別加權(quán)稀疏保持投影,區(qū)別于傳統(tǒng)的稀疏保持投影方法,新算法將聚類與判別加權(quán)稀疏重構(gòu)結(jié)合起來,通過聚類得到每個訓(xùn)練樣本的標(biāo)記,實(shí)現(xiàn)了無監(jiān)督的判別性能,從而在提升簡單性的同時提升了識別精度。(3)提出一種圖嵌入的判別協(xié)同保持投影特征提取算法。本文提出了一種對分類器適應(yīng)的特征提取算法,并將該模型融合到圖嵌入框架。新算法使用協(xié)同表示構(gòu)建類內(nèi)和類間圖,不僅避免了傳統(tǒng)流形學(xué)習(xí)算法的參數(shù)尋優(yōu)困難,而且繼承了協(xié)同表示的魯棒性。通過引入標(biāo)記信息增加了算法的判別性能,從而提升了算法的識別性能。(4)提出一種加權(quán)稀疏表示分類器。稀疏表示分類器SRC通過重構(gòu)誤差來分類測試樣本,但是它同等地對待每一個訓(xùn)練樣本,不能體現(xiàn)樣本之間的差異性。本文提出應(yīng)用預(yù)先定義的重構(gòu)模型的重構(gòu)誤差對每個樣本賦予不同的權(quán)值,然后求解加權(quán)的稀疏重構(gòu)模型。實(shí)驗(yàn)結(jié)果表明,新算法提高了識別精度并降低了時間復(fù)雜度。
[Abstract]:Face recognition is an important research field in the field of pattern recognition. Face images are affected by environment, illumination, expression and posture. Face recognition research is very challenging. How to quickly and accurately use the computer to detect and recognize faces is the key of current face recognition technology. Although some achievements have been made in face recognition research, there are still many problems and key technologies to be further solved and improved, including: face feature extraction adequacy research. That is, how to embed local and global structure information; Research on the classification performance of face feature recognition, that is, the design of high accuracy recognition rate and fast classification algorithm, etc. Face recognition technology based on sparse representation. It has simple theoretical foundation and good robustness. Therefore, this paper studies sparse representation face recognition algorithm, focusing on feature extraction and classification recognition. Some new face feature extraction and recognition algorithms are proposed, and a large number of experiments are carried out on the face reference database. The results show that the algorithm has achieved good results in the computation efficiency and recognition rate of face recognition. The main work and contribution of this paper are as follows: 1). A new feature extraction algorithm based on weighted principal component analysis (PCA) is proposed. Firstly, each feature is weighted by linear fitting marking information and feature dimension. The weight of some features is zero by sparse constraint, and then the feature extraction of principal component analysis (PCA) is carried out. This method realizes feature pre-selection and highlights important feature attributes. The experimental results show that. The new algorithm can not only reduce the computational complexity, but also improve the accuracy of classification.) two sparse preserving projection feature extraction algorithms are proposed, one is weighted sparse neighborhood preserving projection. A weighted sparse reconstruction model is used to learn the reconstruction coefficients, and the time complexity is reduced by limiting the number of non-zero reconstruction coefficients. The recognition accuracy and global robustness are improved. The other is the unsupervised discriminant weighted sparse preserving projection based on clustering, which is different from the traditional sparse preserving projection method. The new algorithm combines clustering with discriminant weighted sparse reconstruction to get the marks of each training sample by clustering to achieve unsupervised discriminant performance. Thus, a feature extraction algorithm of discriminant co-preserving projection based on graph embedding is proposed, and a feature extraction algorithm adapted to the classifier is proposed in this paper. The new algorithm uses cooperative representation to construct intra-class and inter-class graphs, which not only avoids the difficulty of parameter optimization of traditional manifold learning algorithm. Moreover, the robustness of cooperative representation is inherited, and the discriminant performance of the algorithm is improved by introducing label information. A weighted sparse representation classifier is proposed. The sparse representation classifier (SRC) classifies test samples by refactoring errors, but it treats each training sample equally. The reconstruction error of the pre-defined reconstruction model is proposed to assign different weights to each sample, and then to solve the weighted sparse reconstruction model. The new algorithm improves the recognition accuracy and reduces the time complexity.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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相關(guān)博士學(xué)位論文 前1條

1 山世光;人臉識別中若干關(guān)鍵問題的研究[D];中國科學(xué)院研究生院（計算技術(shù)研究所）;2004年

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本文編號：1454572