本文選題：人臉識別　切入點：特征提取　出處：《南昌航空大學》2016年碩士論文　論文類型：學位論文

【摘要】：人臉識別在模式識別與計算機視覺領域中頗受科研人員的熱愛,屬于生物識別的研究范疇。其中,特征提取是模式識別眾多問題中最為重要的一環(huán),人臉識別技術研究的關鍵所在就是如何提取有利于分類的鑒別特征。傳統(tǒng)的全局特征提取方法無法提取人臉圖像的局部特征,傳統(tǒng)的局部提取方法無法顧及人臉圖像的全局特征,并且存在數(shù)據維度過高、樣本數(shù)少和識別效果不理想等問題。本文就基于多流形的特征提取理論和方法做了以下相關研究,主要工作分為以下幾部分:(1)闡述了人臉識別的概述、研究背景及內容、應用及難點等,并簡要介紹了幾種典型的人臉數(shù)據庫。(2)詳細介紹了四種經典的人臉識別特征提取方法:主成分分析(PCA)、線性鑒別分析(LDA)、局部保持投影(LPP)和局部線性嵌入(LLE)方法。并對這四種方法的優(yōu)缺點進行了簡要闡述。(3)在最大間距準則(MMC)算法的基礎上,通過引入多流形思想,提出了基于多流形的最大間距準則局部圖嵌入(MLGE/MMC)算法。此算法首先構造出多流形外部散度,其次通過多流形內部重建權重矩陣構造出多流形內部散度,最后要達到的目的就是使流形外部的間隔可分性最大以及流形內部的變化最小。與此同時,最大限度地擴大流形邊緣,以此更有效地進行特征的提取與分類。此算法采用MMC準則的形式構造目標函數(shù),有效解決了因訓練樣本較少而導致算法的判別能力下降的問題。(4)非監(jiān)督線性差分投影(ULDP)方法能使相距比較遠的數(shù)據點之間的非局部散度達到最大。但ULDP方法也存在著以下不足:1)在學習過程中過分依賴訓練樣本的數(shù)目,當遇到小樣本問題時,就嚴重限制了此方法的應用;2)在提取的眾多特征中,無法揭示了哪些特征對分類與預測起到主導作用。為此,我們提出了基于多流形的非監(jiān)督線性差分投影(MULDP)算法。此算法能得到嵌入在高維空間的低維流形,實現(xiàn)了局部與全局結構信息的有效保持。(5)最后運用MATLAB平臺創(chuàng)建了人臉識別系統(tǒng),來驗證經典方法和本文所提出的方法。
[Abstract]:Face recognition is loved by researchers in the field of pattern recognition and computer vision, and it belongs to the research field of biometrics, in which feature extraction is the most important part of pattern recognition. The key of face recognition research is how to extract the discriminant features which are favorable to classification. The traditional global feature extraction method can not extract the local features of face image. The traditional local extraction method can not take into account the global features of face image, and there are many problems such as too high data dimension, fewer samples and less recognition effect. In this paper, the theory and method of feature extraction based on multi-manifold are studied as follows. The main work is divided into the following parts: 1) the overview of face recognition, research background and content, application and difficulties, etc. This paper briefly introduces several typical face database. It introduces in detail four classical face recognition feature extraction methods: principal component analysis (PCA), linear discriminant analysis (LDAA), local preserving projection (LPP) and local linear embedding (LLEs). In this paper, the advantages and disadvantages of these four methods are briefly described. Based on the maximum spacing criterion (MMC) algorithm, this paper gives a brief description of the advantages and disadvantages of the four methods. By introducing the idea of multi-manifold, a local graph embedding algorithm based on the maximum distance criterion of multi-manifold (MLGE / MMC) is proposed. Firstly, the external divergence of multi-manifold is constructed, and then the internal divergence of multi-manifold is constructed by reconstructing the weight matrix of multi-manifold. Finally, the goal to be achieved is to maximize the outer separability of the manifold and minimize the internal variation of the manifold. At the same time, the edge of the manifold is maximized. The algorithm uses the form of MMC criterion to construct the objective function. It effectively solves the problem that the discriminant ability of the algorithm is reduced due to the small number of training samples. (4) the unsupervised linear differential projection (ULDP-) method can maximize the nonlocal divergence between data points far away from each other, but the ULDP method also exists. In the following less than 1: 1) excessive reliance on the number of training samples in the learning process, When the problem of small samples is encountered, the application of this method is severely restricted. Among the many features extracted, it is impossible to reveal which features play a leading role in classification and prediction. We propose an unsupervised linear differential projection (MULDP) algorithm based on multimanifold, which can obtain low dimensional manifold embedded in high dimensional space. Finally, a face recognition system based on MATLAB platform is created to verify the classical method and the method proposed in this paper.
【學位授予單位】：南昌航空大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41

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