本文關(guān)鍵詞： 人臉正面化 正交Procrustes Shatten-p范數(shù) 三維模型　出處：《南京理工大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：一直以來,人臉識(shí)別都是模式識(shí)別和計(jì)算機(jī)視覺領(lǐng)域的熱點(diǎn)問題,盡管在正面視圖上人臉識(shí)別已經(jīng)取得了很好的結(jié)果,但是存在不同姿態(tài)的人臉圖像仍然會(huì)導(dǎo)致人臉識(shí)別系統(tǒng)性能的下降。為了將不同姿態(tài)下的人臉圖像轉(zhuǎn)換成正面視圖,本文圍繞人臉正面化問題,運(yùn)用統(tǒng)計(jì)分析、三維建模、特征表示、回歸表示等理論,歸納總結(jié)了當(dāng)前主流的人臉正面化方法,并對(duì)現(xiàn)有算法加以改進(jìn),以提升人臉正面化方法的魯棒性。本文的主要工作和研究成果如下:(1)從基于二維平面的人臉正面化方法出發(fā),本文提出了結(jié)構(gòu)化正交Procrustes回歸。與正交Procrustes回歸用Frobenius范數(shù)約束誤差項(xiàng)相比,我們的方法用Shatten-p范數(shù)可以更好地刻畫圖像的結(jié)構(gòu)信息,從而能夠得到更加魯棒的結(jié)果。此外我們也分別討論使用l1范數(shù)和l2范數(shù)來約束表示系數(shù)的情況,并給出了相應(yīng)的優(yōu)化算法。(2)本文提出了 一種基于三維模型的魯棒人臉正面化方法,該方法首先應(yīng)用SDM(Supervised Descent Method)方法對(duì)給定的二維圖像進(jìn)行特征點(diǎn)定位。同時(shí),給定一個(gè)正面的三維模型并手動(dòng)標(biāo)定相應(yīng)的特征點(diǎn),通過二維圖像和三維模型之間的特征點(diǎn)位置對(duì)應(yīng)關(guān)系,我們可以計(jì)算出二維圖像特征點(diǎn)和三維模型特征點(diǎn)間的投影矩陣,利用該投影矩陣初步將二維圖像的姿態(tài)變化矯正為正面視圖。然后,再利用人臉的局部對(duì)稱性完成對(duì)不可視區(qū)域填充,同時(shí)進(jìn)行遮擋檢測,并利用泊松圖像編輯和局部對(duì)稱性去除遮擋。
[Abstract]:Face recognition has always been a hot topic in the field of pattern recognition and computer vision, although it has achieved good results in frontal view. But face images with different pose still lead to the deterioration of face recognition system performance. In order to convert face images under different poses into positive view, this paper uses statistical analysis around face frontal image. Three-dimensional modeling, feature representation, regression representation and other theories, summarized the current mainstream face frontal methods, and improved the existing algorithms. In order to improve the robustness of the face obverse method. The main work and research results are as follows: 1) based on the two-dimensional plane face obverse method. In this paper, a structured orthogonal Procrustes regression is proposed, which is compared with the Frobenius norm constraint error term used in orthogonal Procrustes regression. Our method can better depict the structure information of images by using Shatten-p norm. In addition, we also discuss the case of using l 1 norm and l 2 norm to constrain the coefficients respectively. In this paper, a robust face facade method based on 3D model is proposed. Firstly, the SDM(Supervised Descent method is used to locate the feature points of a given two-dimensional image. A frontal 3D model is given and the corresponding feature points are manually calibrated. We can calculate the projection matrix between 2D image feature points and 3D model feature points, using this projection matrix, we can preliminarily correct the attitude change of 2D image into a frontal view. Then the local symmetry of the face is used to fill the invisible region, and the occlusion detection is carried out at the same time, and the occlusion is removed by Poisson image editing and local symmetry.
【學(xué)位授予單位】：南京理工大學(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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相關(guān)期刊論文 前1條

1 陳曉鋼;陸玲;周書民;劉向陽;;一種新的人臉姿態(tài)估計(jì)算法[J];數(shù)據(jù)采集與處理;2009年04期

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本文編號(hào)：1464101