發(fā)布時(shí)間：2018-06-18 10:17

  本文選題：人臉識(shí)別 + 單演二值編碼��； 參考：《湖南師范大學(xué)》2016年碩士論文

【摘要】：人臉識(shí)別在機(jī)器學(xué)習(xí)與數(shù)字圖像處理領(lǐng)域是一個(gè)非常受歡迎但又極具有挑戰(zhàn)性的課題。因?yàn)樗性S多優(yōu)點(diǎn),比如非接觸性、隱蔽性、以及圖像采集設(shè)備成本低等,已經(jīng)被越來(lái)越多地應(yīng)用于信息安全、人機(jī)交互、人工智能以及電子商務(wù)等安全中。但在實(shí)際應(yīng)用中,許多非可控條件比如遮擋、姿態(tài)變化、光照、表情和時(shí)間等制約人臉識(shí)別的性能,特別是條件改變厲害的時(shí)候,性能較好的識(shí)別系統(tǒng)識(shí)別率也會(huì)快速下降。文章從特征提取、數(shù)據(jù)降維和分類(lèi)識(shí)別三個(gè)方面對(duì)人臉識(shí)別算法展開(kāi)廣泛的研究,論文的主要工作包括:(1)對(duì)人臉識(shí)別這一問(wèn)題進(jìn)行了簡(jiǎn)單的描述,綜合分析和概括了人臉識(shí)別技術(shù)目前的發(fā)展現(xiàn)狀。(2)總結(jié)了已有的全局特征提取與局部特征提取算法,全局特征對(duì)姿態(tài)變化和遮擋非常敏感,一般用來(lái)進(jìn)行粗略的匹配,而局部特征一般為人臉識(shí)別提供細(xì)致的確認(rèn)。(3)研究了基于單演二值編碼的人臉識(shí)別算法,良好的人臉特征提取算法是魯棒高效的人臉識(shí)別算法能否成功的關(guān)鍵因素,融合局部幅值、相位以及方向的單演二值編碼是目前最好的特征提取方法之一。(4)研究了基于稀疏編碼的人臉識(shí)別算法,稀疏編碼算法有多種模型,主要研究了基于稀疏表示與基于協(xié)同表示的人臉識(shí)別算法,基于稀疏表示的人臉識(shí)別算法是以最小的重構(gòu)誤差來(lái)進(jìn)行分類(lèi)的,但是稀疏表示的計(jì)算代價(jià)非常高�；趨f(xié)同表示的人臉識(shí)別算法是稀疏編碼的幾種模型中速度最快的之一。(5)盡管基于稀疏表示的人臉識(shí)別算法非常新穎有效,但是有一個(gè)問(wèn)題需要進(jìn)一步解決。用來(lái)測(cè)試的特征臉、隨機(jī)臉和Fisher臉都是全局特征,因?yàn)樵趯?shí)際應(yīng)用中,訓(xùn)練樣本通常都是受到限制的,這樣的全局特征不能有效地處理光照、表情和姿勢(shì)等變化。為此,提出一種新穎的融合單演二值編碼與稀疏編碼的人臉識(shí)別算法,在提取人臉局部特征之后,采用稀疏表示的方法降維,考慮到算法的識(shí)別率與算法運(yùn)行時(shí)間,采用稀疏編碼模型中的協(xié)同表示模型。在ORL、AR和PolyU-NIR人臉庫(kù)上測(cè)試,實(shí)驗(yàn)結(jié)果表明,該算法相較于傳統(tǒng)的稀疏編碼算法,性能有所改善。
[Abstract]:Face recognition is a very popular and challenging topic in the field of machine learning and digital image processing. Because it has many advantages, such as non-contact, concealment, and low cost of image acquisition equipment, it has been increasingly used in information security, human-computer interaction, artificial intelligence and e-commerce security. However, in practical applications, many uncontrollable conditions such as occlusion, attitude change, illumination, facial expression and time restrict the performance of face recognition, especially when the conditions change severely, the recognition rate of the recognition system with better performance will decline rapidly. In this paper, face recognition algorithms are widely studied from three aspects: feature extraction, data reduction and classification recognition. The main work of this paper includes: 1) briefly describing the problem of face recognition. The present development of face recognition technology is analyzed and summarized. The existing algorithms of global feature extraction and local feature extraction are summarized. The global feature is very sensitive to the change of posture and occlusion, and is generally used for rough matching. However, local features generally provide detailed recognition for face recognition. (3) A face recognition algorithm based on single binary coding is studied. A good face feature extraction algorithm is a key factor to the success of robust and efficient face recognition algorithm. Fusion of local amplitude, phase and direction is one of the best feature extraction methods. (4) face recognition algorithm based on sparse coding is studied. There are many models in sparse coding algorithm. Face recognition algorithms based on sparse representation and cooperative representation are mainly studied. The face recognition algorithm based on sparse representation is classified with minimal reconstruction error, but the computational cost of sparse representation is very high. The face recognition algorithm based on cooperative representation is one of the fastest among several models of sparse coding. Although the face recognition algorithm based on sparse representation is novel and effective, there is one problem that needs to be solved further. The feature faces, random faces and Fisher faces used for testing are all global features, because in practical applications, the training samples are usually restricted, so the global features can not effectively deal with the changes of illumination, expression and posture. In this paper, a novel face recognition algorithm combining single binary coding and sparse coding is proposed. After extracting the local features of the face, the method of sparse representation is used to reduce the dimension, considering the recognition rate of the algorithm and the running time of the algorithm. The cooperative representation model in sparse coding model is adopted. The experimental results on ORLPAR and PolyU-NIR face databases show that the performance of the proposed algorithm is better than that of the traditional sparse coding algorithm.
【學(xué)位授予單位】：湖南師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.41
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本文編號(hào)：2035134