本文選題：圖像自適應(yīng)分塊 + SIFT��； 參考：《上海師范大學(xué)》2017年碩士論文

【摘要】：隨著科技的進(jìn)步和多媒體技術(shù)的迅速發(fā)展,人臉識(shí)別技術(shù)在各個(gè)場(chǎng)景中的應(yīng)用越來(lái)越多,特別是在安全管理領(lǐng)域的應(yīng)用,更加凸顯了人臉識(shí)別技術(shù)的重要性。人臉識(shí)別技術(shù)的關(guān)鍵是從人臉圖像中提出有效的特征信息,這些有效的特征信息是實(shí)現(xiàn)人臉匹配識(shí)別的重要因素之一。因此,如何在信息量龐大的人臉圖像中提取出最有效的特征信息,成為人臉識(shí)別技術(shù)中研究的熱點(diǎn)。本文主要研究基于高鑒別力SIFT(Scale-invariant feature transform)和LGQP(Local Gabor Quaternary Pattern)的人臉識(shí)別,對(duì)傳統(tǒng)的特征提取方法做出了相應(yīng)的改進(jìn)。本文研究的問(wèn)題主要由以下三個(gè)方面構(gòu)成:1.提出一種新的圖像自適應(yīng)分塊策略,進(jìn)一步提高識(shí)別算法的效率。該方法根據(jù)人臉圖像中SIFT特征點(diǎn)的分布情況,對(duì)人臉圖像分塊,使人臉五官分別位于同一個(gè)子塊內(nèi),提高子塊的可識(shí)別力,降低頭部姿勢(shì)變化和面部表情變化對(duì)識(shí)別結(jié)果的影響。本文選取了ORL、YALE和JAFFE人臉庫(kù)作為實(shí)驗(yàn)對(duì)象,實(shí)驗(yàn)結(jié)果證明了該方法的有效性。2.在傳統(tǒng)LBP(Local Binary Pattern)算子的基礎(chǔ)上,利用局部區(qū)域的均值和標(biāo)準(zhǔn)差對(duì)周?chē)袼剡M(jìn)行灰度變化運(yùn)算,提出一種四值模式的LQP(Local Quaternary Pattern)算子,并將該算子與Gabor濾波結(jié)合,用于在人臉圖像中提取LGQP特征。實(shí)驗(yàn)結(jié)果表明LGQP特征具有比LGBP特征和LGTP特征更加穩(wěn)定和可靠的特性。3.借鑒Fisher線性判別分析的思想,本文提出一種利用類(lèi)間與類(lèi)內(nèi)相關(guān)系數(shù)計(jì)算SIFT關(guān)鍵點(diǎn)鑒別力的算法。使用該算法計(jì)算出SIFT關(guān)鍵點(diǎn)的鑒別力之后,再計(jì)算出該點(diǎn)在不同方向、不同尺度的LGQP特征,并將LGQP特征與SIFT特征結(jié)合在一起,完成最終的人臉匹配識(shí)別。
[Abstract]:With the rapid development of science and technology and the rapid development of multimedia technology, the application of face recognition technology in every scene is more and more, especially in the field of security management, which highlights the importance of face recognition technology. The key of face recognition technology is to put forward effective feature information from face image, and these effective feature letters are the key to face recognition technology. Interest is one of the important factors to realize face matching recognition. Therefore, how to extract the most effective feature information in a large face image has become a hot spot in the research of face recognition technology. This paper mainly studies people based on high discriminability SIFT (Scale-invariant feature transform) and LGQP (Local Gabor Quaternary Pattern). Face recognition has made a corresponding improvement to traditional feature extraction methods. The main problems studied in this paper are mainly composed of the following three aspects: 1. a new image adaptive partitioning strategy is proposed to further improve the efficiency of the recognition algorithm. The method is based on the distribution of SIFT feature points in the face image, block the face image and make face five. In this paper, ORL, YALE and JAFFE face database are selected as experimental objects. The experimental results show that the effectiveness of the method.2. is based on the traditional LBP (Local Binary Pattern) operator and uses the Bureau. The mean and standard deviation of the region are calculated by the gray change of the surrounding pixels, and a LQP (Local Quaternary Pattern) operator of the four value mode is proposed, and the operator is combined with the Gabor filter to extract the LGQP features in the face image. The experimental results show that the LGQP feature has a more stable and reliable specificity than the LGBP and LGTP features. .3. draws on the idea of Fisher linear discriminant analysis. This paper presents an algorithm for calculating the discriminability of SIFT key points using the correlation coefficient between classes and classes. After using this algorithm to calculate the discriminability of the key points of the SIFT, the LGQP characteristics of the point in different directions and different scales are calculated, and the LGQP features are combined with the SIFT features. Final face matching recognition.

【學(xué)位授予單位】：上海師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前7條

1 謝瓊裕;戴群;;融合Gabor和LGBP的單樣本人臉識(shí)別[J];小型微型計(jì)算機(jī)系統(tǒng);2014年07期

2 傅衛(wèi)平;秦川;劉佳;楊世強(qiáng);王雯;;基于SIFT算法的圖像目標(biāo)匹配與定位[J];儀器儀表學(xué)報(bào);2011年01期

3 鄭永斌;黃新生;豐松江;;SIFT和旋轉(zhuǎn)不變LBP相結(jié)合的圖像匹配算法[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);2010年02期

4 劉立;彭復(fù)員;趙坤;萬(wàn)亞平;;采用簡(jiǎn)化SIFT算法實(shí)現(xiàn)快速圖像匹配[J];紅外與激光工程;2008年01期

5 張文超;山世光;張洪明;陳杰;陳熙霖;高文;;基于局部Gabor變化直方圖序列的人臉描述與識(shí)別[J];軟件學(xué)報(bào);2006年12期

6 周杰,盧春雨,張長(zhǎng)水,李衍達(dá);人臉自動(dòng)識(shí)別方法綜述[J];電子學(xué)報(bào);2000年04期

7 周激流,張曄;人臉識(shí)別理論研究進(jìn)展[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);1999年02期

相關(guān)博士學(xué)位論文 前3條

1 宋濤;基于局部特征的人臉圖像分析和識(shí)別方法研究[D];浙江大學(xué);2015年

2 余磊;Gabor小波變換在人臉識(shí)別中的應(yīng)用研究[D];重慶大學(xué);2009年

3 山世光;人臉識(shí)別中若干關(guān)鍵問(wèn)題的研究[D];中國(guó)科學(xué)院研究生院（計(jì)算技術(shù)研究所）;2004年

相關(guān)碩士學(xué)位論文 前3條

1 于祥春;一種基于SIFT的特征提取在人臉識(shí)別算法中的研究[D];吉林大學(xué);2015年

2 胡國(guó)靖;人臉識(shí)別中圖像預(yù)處理與匹配方法研究[D];南京理工大學(xué);2014年

3 黃非非;基于LBP的人臉識(shí)別研究[D];重慶大學(xué);2009年

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本文編號(hào)：1806836