本文選題：三維人臉建模 + 單張圖片��； 參考：《南京理工大學(xué)》2007年碩士論文

【摘要】： 三維人臉模型的應(yīng)用廣泛存在于安全認(rèn)證、影視動(dòng)漫、醫(yī)學(xué)科學(xué)等領(lǐng)域。近年來(lái)，以詳盡的臉部信息進(jìn)行三維人臉重建取得了許多成果。然而詳盡的臉部信息獲取不僅成本昂貴，更有諸如監(jiān)控視頻的特定任務(wù)的檢索等應(yīng)用卻因?qū)ο笤驘o(wú)法采集更多的臉部信息。鑒此，本文將進(jìn)行基于單張正面照片信息進(jìn)行臉部三維重建方法的討論。 首先從大量的正側(cè)面臉部照片采集著手構(gòu)建了正、側(cè)面臉部信息的人臉庫(kù)，依據(jù)人體測(cè)量學(xué)、人體解剖學(xué)等臉部關(guān)鍵特征的原則定義了臉部測(cè)量點(diǎn)及測(cè)量項(xiàng)目。對(duì)提取的正側(cè)面臉部數(shù)據(jù)進(jìn)行神經(jīng)網(wǎng)絡(luò)訓(xùn)練，通過(guò)訓(xùn)練的權(quán)值得到個(gè)體的正面數(shù)據(jù)點(diǎn)的臉部深度數(shù)據(jù)，擬合及仿真的實(shí)驗(yàn)結(jié)果表明了方法的可行性。 其次針對(duì)神經(jīng)網(wǎng)絡(luò)訓(xùn)練過(guò)程中收斂速度慢的不足，探討了尋找最優(yōu)形狀因子、最優(yōu)學(xué)習(xí)率以及兩者最優(yōu)組合的加速算法，結(jié)合基于epsilon向量外推的加速方法得到一種新的加速算法，數(shù)值試驗(yàn)表明能使速度和精度顯著提高。 第三，基于建立的人臉數(shù)據(jù)庫(kù)中測(cè)量點(diǎn)定義模型的正側(cè)面特征點(diǎn)，采用徑向基函數(shù)插值的方法對(duì)模型進(jìn)行調(diào)整，，生成特定人臉模型。對(duì)于粗糙的原始網(wǎng)格給出了一種改進(jìn)的Loop細(xì)分方法，使細(xì)分后的模型更符合原有形狀，并紋理映射生成具有真實(shí)感的人臉模型。 最后用Visual C++.NET和OpenGL實(shí)現(xiàn)了從臉部數(shù)據(jù)庫(kù)數(shù)據(jù)提取到三維人臉重建系統(tǒng)。
[Abstract]:Three-dimensional face models are widely used in the fields of security authentication, video animation, medical science and so on. In recent years, many achievements have been made in 3D face reconstruction with detailed facial information. However, detailed facial information acquisition is not only expensive, but also can not collect more facial information because of object reasons. In view of this, this paper will discuss the method of facial 3D reconstruction based on single front photo information. Firstly, the face database of face information is constructed from the collection of a large number of face photographs on the front and side sides. According to the principles of anthropometry, human anatomy and other key features of the face, the measurement points and measurement items of the face are defined. Neural network training is carried out on the extracted facial data, and the depth data of the face of the positive data points are obtained by the weights of the training. The experimental results of fitting and simulation show the feasibility of the method. Secondly, aiming at the shortage of slow convergence in the training process of neural network, the paper discusses the accelerated algorithm to find the optimal shape factor, the optimal learning rate and the optimal combination of the two. A new acceleration algorithm based on epsilon vector extrapolation is presented. The numerical results show that the speed and accuracy can be improved significantly. Thirdly, the radial basis function (RBF) interpolation method is used to adjust the model to generate a specific face model based on the positive side feature points of the measurement point definition model in the established face database. An improved Loop subdivision method is proposed for rough original meshes to make the subdivided models more consistent with the original shape and texture mapping to generate realistic face models. Finally, Visual C. Net and OpenGL are used to realize the face data extraction and 3D face reconstruction system.
【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】：2007
【分類(lèi)號(hào)】：TP391.41

【引證文獻(xiàn)】

相關(guān)期刊論文 前1條

1 金彪;姚志強(qiáng);;基于單幅人臉正視圖的個(gè)性化人臉三維重建[J];福建師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年01期

相關(guān)碩士學(xué)位論文 前6條

1 方恂;基于單幅照片的三維人臉重建[D];中國(guó)地質(zhì)大學(xué)（北京）;2011年

2 郭洋;基于神經(jīng)網(wǎng)絡(luò)的單張照片三維人臉建模[D];北京郵電大學(xué);2011年

3 徐雪絨;基于單張正面照片的三維人臉建模及表情合成的研究[D];西南交通大學(xué);2011年

4 金彪;基于單幅圖像的個(gè)性化人臉建模研究[D];福建師范大學(xué);2011年

5 林牧;近紅外成像下的人臉特征提取和三維重建關(guān)鍵技術(shù)的研究[D];浙江大學(xué);2008年

6 吳果強(qiáng);三維人臉重構(gòu)與反求技術(shù)研究[D];南昌大學(xué);2010年

本文編號(hào)：1879736