本文選題：人臉識(shí)別 + 稀疏表示��； 參考：《安徽大學(xué)》2016年碩士論文

【摘要】：人臉識(shí)別是生物特征識(shí)別中的一項(xiàng)重要技術(shù),也是圖像處理,機(jī)器學(xué)習(xí)等領(lǐng)域的熱點(diǎn)研究課題,在公安系統(tǒng),保險(xiǎn),銀行,海關(guān),身份證系統(tǒng)等領(lǐng)域具有廣闊的應(yīng)用前景。不同于傳統(tǒng)的Eigenface, fisherface算法,2009年Wright等人提出了基于稀疏表示的人臉識(shí)別(Sparse Representation-based Classification, SRC),由于其對(duì)噪聲有較好的魯棒性,在人臉識(shí)別上取得巨大的成功,并將人臉識(shí)別引入了一個(gè)新的發(fā)展方向。本文主要對(duì)稀疏表示的人臉識(shí)別進(jìn)行了研究,論文的主要工作如下：(1)加權(quán)稀疏近鄰表示的人臉識(shí)別(WSNRC)。在每一類訓(xùn)練樣本中尋找與測(cè)試樣本最近的k個(gè)樣本構(gòu)成此類新的訓(xùn)練字典,然后在求解l1范數(shù)最小化的稀疏系數(shù)時(shí),為每一個(gè)新的訓(xùn)練樣本對(duì)應(yīng)的稀疏系數(shù)賦上一個(gè)權(quán)值；最后在新的字典下,根據(jù)最小的重構(gòu)誤差來(lái)完成識(shí)別任務(wù)。在Yale B數(shù)據(jù)庫(kù)和ORL數(shù)據(jù)庫(kù)上的大量實(shí)驗(yàn)結(jié)果表明,WSNRC與NN算法和稀疏近鄰表示(SNRC)算法相比,取得了較高的識(shí)別率,證明了該方法的有效性。(2)正則化Fisher分析和稀疏表示的人臉識(shí)別。首先使用正則化Fisher分析算法從訓(xùn)練樣本中提取出最優(yōu)投影矩陣,然后將訓(xùn)練樣本和測(cè)試樣本在投影矩陣下投影獲得其低維表示,最后使用稀疏表示分類器進(jìn)行人臉識(shí)別,在AR數(shù)據(jù)庫(kù)和擴(kuò)展的YaleB數(shù)據(jù)庫(kù)上的大量實(shí)驗(yàn)結(jié)果表明,正則化Fisher分析和稀疏表示結(jié)合的方法取得較好的效果。(3)結(jié)合Gabor特征和對(duì)稱臉的稀疏表示人臉識(shí)別。首先根據(jù)原始訓(xùn)練樣本獲得其對(duì)應(yīng)的虛擬對(duì)稱臉,然后將原訓(xùn)練樣本和對(duì)稱臉結(jié)合起來(lái)構(gòu)成新的訓(xùn)練樣本,最后提取訓(xùn)練樣本和測(cè)試樣本的Gabor特征并使用SRC分類器進(jìn)行人臉識(shí)別。在ORL數(shù)據(jù)庫(kù),Yale數(shù)據(jù)庫(kù),FERET數(shù)據(jù)庫(kù)上的實(shí)驗(yàn)表明了GMSRC的有效性。
[Abstract]:Face recognition is an important technology in biometrics. It is also a hot research topic in image processing, machine learning and other fields. It has broad application prospects in public security system, insurance, bank, customs, identity card system and other fields. It is different from the traditional Eigenface, Fisherface algorithm, and in 2009, Wright and others proposed a sparse table. Sparse Representation-based Classification (SRC), because of its good robustness to noise, has achieved great success in face recognition, and introduces face recognition to a new direction. This paper mainly studies the face recognition of sparse representation. The main work of this paper is as follows: (1) weighting Face recognition (WSNRC) of sparse near neighbor representation. In each class of training samples, searching for the nearest K sample with the test sample constitutes such a new training dictionary, and then a weight is assigned to the sparse coefficient corresponding to each new training sample when the sparse coefficient of the L1 norm minimized, and finally under the new dictionary, according to the minimum. A large number of experimental results on the Yale B database and the ORL database show that WSNRC has a higher recognition rate compared with the NN algorithm and the sparse nearest neighbor representation (SNRC) algorithm. (2) the regularized Fisher analysis and the sparse representation of the face recognition. First, the regularized Fisher is used. The algorithm extracts the optimal projection matrix from the training sample. Then the training sample and the test sample are projected under the projection matrix to obtain their low dimensional representation. Finally, the sparse representation classifier is used for face recognition. A large number of actual results on the AR database and the extended YaleB database show that the regularized Fisher analysis and sparse representation are regularized. The combined method has good results. (3) face recognition based on the sparse representation of Gabor features and symmetrical faces. First, the corresponding virtual symmetry faces are obtained according to the original training samples. Then the original training samples and symmetrical faces are combined to form a new training sample. Finally, the Gabor features of the training samples and the test samples are extracted and the SRC is used. The classifier is used for face recognition. Experiments on ORL database, Yale database and FERET database show the effectiveness of GMSRC.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41

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本文編號(hào)：1970954