本文選題：人臉識別 + 稀疏表示��； 參考：《大連海事大學(xué)》2014年碩士論文

【摘要】：人臉識別是計算機(jī)技術(shù)研究領(lǐng)域的一項熱門學(xué)科,它屬于生物特征識別技術(shù),利用生物個體自身特征實現(xiàn)對生物體的區(qū)分與識別。人臉識別由于自身的優(yōu)越性以及在多媒體、模式識別、圖像處理、計算機(jī)視覺等領(lǐng)域的廣泛運用,近些年來人臉識別技術(shù)得到了長足的進(jìn)步,大量的人臉識別系統(tǒng)應(yīng)用在身份鑒別上。 壓縮感知理論是由Donoho與Candes等人于2006年提出的一個新的理論框架,很快被引入到人臉識別領(lǐng)域并引發(fā)了一陣研究熱潮,該理論應(yīng)用最成功的要數(shù)基于稀疏表示的人臉識別算法(Sparse Representation-based Classification, SRC)和已經(jīng)存在的大多數(shù)算法相比較,SRC方法利用了高維數(shù)據(jù)分布的稀疏性特點,能夠較為有效地應(yīng)付人臉圖像的高維數(shù)問題。此外,SRC方法處理的過程利用的是圖像的原始像素,大大減小了一些預(yù)處理操作導(dǎo)致的信息丟失。但是,人臉姿態(tài)及表情的變化容易引起對齊誤差,而現(xiàn)有SRC方法要求訓(xùn)練圖像和測試圖像嚴(yán)格對齊,這也影響了SRC方法的性能,成為了阻礙SRC方法走向?qū)嵱玫闹饕蛩亍?本論文首先研究了基于稀疏表示的人臉識別算法,介紹了傳統(tǒng)人臉識別中常用的PCA、LDA方法并進(jìn)行了一些仿真試驗,然后通過引入壓縮感知理論實現(xiàn)了基于稀疏表示的人臉識別算法并取得了不錯的識別效果。論文給出了上述稀疏表示方法的基本原理、實現(xiàn)方法,并且用該方法及傳統(tǒng)方法對ORL人臉數(shù)據(jù)庫進(jìn)行了仿真,分別計算出了識別率,比較和揭示了這些方法之間的區(qū)別和聯(lián)系。 在上述研究的基礎(chǔ)上,針對SRC方法的固有缺點實現(xiàn)了基于兩階段稀疏表示的人臉識別,通過在實驗證明兩階段稀疏表示方法相對于傳統(tǒng)SRC方法的優(yōu)勢,然后針對兩階段稀疏表示方法應(yīng)用過程中常常訓(xùn)練樣本不足的問題實現(xiàn)了基于改進(jìn)人臉庫的兩階段稀疏表示方法。本文通過在YALE、FERET和AR庫上的仿真實驗證明了的本方法的有效性,同時該方法對最鄰近樣本數(shù)的依賴有所減弱,魯棒性有所增強(qiáng)。
[Abstract]:Face recognition is a hot subject in the field of computer technology. It belongs to biometric recognition technology. It uses biological individuals' own characteristics to distinguish and recognize organisms.Due to its superiority and its wide application in multimedia, pattern recognition, image processing and computer vision, face recognition technology has made great progress in recent years.A large number of face recognition systems are used in identification.The theory of compressed perception is a new theoretical framework proposed by Donoho and Candes et al in 2006. It was quickly introduced into the field of face recognition and triggered a wave of research.In this theory, the most successful face recognition algorithm based on sparse representation is Sparse Representation-based Classification (SRCCs), which makes use of the sparsity of high-dimensional data distribution in comparison with most existing algorithms.It can deal with the problem of high dimension of face image effectively.In addition, the SRC method uses the original pixels of the image, which greatly reduces the loss of information caused by some preprocessing operations.However, the change of face pose and expression is easy to cause alignment error, and the existing SRC methods require strict alignment of training and test images, which also affects the performance of SRC method and becomes the main factor that hinders the application of SRC method.In this paper, the algorithm of face recognition based on sparse representation is studied, and the PCA-LDA method, which is commonly used in traditional face recognition, is introduced, and some simulation experiments are carried out.Then a face recognition algorithm based on sparse representation is implemented by introducing the theory of compressed perception and good recognition results are obtained.In this paper, the basic principle and realization method of the above sparse representation methods are given, and the ORL face database is simulated by this method and the traditional method. The recognition rate is calculated, and the differences and relations between these methods are compared and revealed.On the basis of the above research, face recognition based on two-stage sparse representation is realized in view of the inherent shortcomings of SRC method. It is proved by experiments that two-stage sparse representation method is superior to traditional SRC method.Then, a two-stage sparse representation method based on improved face database is implemented to solve the problem of insufficient training samples in the application of two-stage sparse representation.In this paper, the effectiveness of the proposed method is proved by simulation experiments on the Yaalehnet and AR libraries, and the dependence of the method on the nearest sample number is weakened, and the robustness is enhanced.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP391.41

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本文編號：1770262