本文關鍵詞： 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》2016年碩士論文　論文類型：學位論文

【摘要】：一直以來,人臉識別都是模式識別和計算機視覺領域的熱點問題,盡管在正面視圖上人臉識別已經(jīng)取得了很好的結果,但是存在不同姿態(tài)的人臉圖像仍然會導致人臉識別系統(tǒng)性能的下降。為了將不同姿態(tài)下的人臉圖像轉換成正面視圖,本文圍繞人臉正面化問題,運用統(tǒng)計分析、三維建模、特征表示、回歸表示等理論,歸納總結了當前主流的人臉正面化方法,并對現(xiàn)有算法加以改進,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),本文提出了結構化正交Procrustes回歸。與正交Procrustes回歸用Frobenius范數(shù)約束誤差項相比,我們的方法用Shatten-p范數(shù)可以更好地刻畫圖像的結構信息,從而能夠得到更加魯棒的結果。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,該方法首先應用SDM(Supervised Descent Method)方法對給定的二維圖像進行特征點定位。同時,給定一個正面的三維模型并手動標定相應的特征點,通過二維圖像和三維模型之間的特征點位置對應關系,我們可以計算出二維圖像特征點和三維模型特征點間的投影矩陣,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稱性完成對不可視區(qū)域填充,同時進行遮擋檢測,并利用泊松圖像編輯和局部對稱性去除遮擋。
[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.
【學位授予單位】：南京理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41

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本文編號：1464101