利用單一立體參考模型之多角度人臉辨識｜國立臺灣科技大學博碩士論文系統

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研究生：	李玲瑩 Ling-ying Lee
論文名稱：	利用單一立體參考模型之多角度人臉辨識 Face Recognition Across Poses Using A Single Reference Model
指導教授：	徐繼聖 Gee-sern Hsu
口試委員:	林昌鴻 Chang-hong Lin 鍾聖倫 Sheng-luen Chung 楊士萱 Shih-hsuan Yang
學位類別：	碩士 Master
系所名稱：	工程學院 - 機械工程系 Department of Mechanical Engineering
論文出版年：	2012
畢業學年度：	100
語文別：	中文
論文頁數：	62
中文關鍵詞：	三維人臉辨識、三維人臉重建
外文關鍵詞：	Face Recognition Across Poses, 3D Face Reconstruction
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有別於一般以重建人臉三維模型為基礎的多角度人臉辨識研究，本研究藉由引入單一人臉立體模型，根據單一正面註冊影像即可重建出對應的人臉三維模型。相較需要上百幅雷射掃描人臉的3DMorphable Models 、需要多張不同光源條件影像的Illumination Cone或需要精確人臉對稱資訊的Symmetric Shape From Shading方法，本研究在實踐成本、資料量及實際應用上具有優勢。

本研究首先實踐 Kemelmacher-Shlizerman和Basri在 2011年發表的三維人臉重建方法，其方法基於人臉為朗伯表面的假設，以人臉輻射度模型為主軸，根據單一二維影像變型參考模型，得到對應的人臉三維模型。本研究接著將重建模型進行空間座標轉換及平面投影，建立人臉各種角度樣本資料庫，提升系統對角度變化的容忍度，最後擷取LBP特徵搭配SVM分類器進行多角度人臉辨識。

本研究以FRGC資料庫建立參考模型，透過在PIE資料庫的實驗數據，顯示本研究以單一立體參考模型及單一二維影像需求的方法可以達到基本的辨識效能，並且呈現引用多個立體參考模型對大角度辨識率提升的可能性。

Given a frontal facial image as a gallery sample, a scheme is developed to generate novel views of the face for recognition across poses. The core part of the scheme is a recently published 3D face reconstruction which exploits a single reference 3D face model to build a 3D shape model for each face in the gallery set. The 3D shape model combined with the texture of each facial image in the gallery allows novel poses of the face to be generated. The LBP features are then extracted from these generated poses to train an SVM classifier for recognition.

Assuming Lambertian surface with a reflectance function approximated by spherical harmonics, the 3D reference model would be made to deform so that the 2D projection of the deformed model can approximate the facial image in the gallery. The problem is cast as an image irradiance equation with unknown lighting, albedo, and surface normals. Using the reference model to estimate lighting, and providing an initial estimate of albedo, the reflectance function becomes only a function of the unknown surface normals, and the irradiance equation becomes a partial differential equation which is then solved for depth.

A 3D face from the FRGC database is used as the reference model in the experiments, and the performance is evaluated on the PIE database. It is shown that the developed scheme gives a satisfactory performance, and can be further improved if the alignment between the reference model and the gallery image can be enhanced.

教授推薦書 . . . . . . . . . . . . . . . . .  . . . . . . . i
論文口試委員審定書 . . . . . . . . . . . . . . . . . . . . . ii
中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . iii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . .iv
致謝 . . . . . . . . . . . . . . . . . . . . . . . . . . . v
目錄 . . . . . . . . . . . . . . . . . . . . . . . . . .  vi
表格索引 . . . . . . . . . . . . . . . . . . . . . . . . . ix
圖索引. . . . . . . . . . . . . . . . . . . . . . . . . . . x
緒論 . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1 動機與目的. . . . . . . . . . . . . . . . . . . . . . . 1
2 相關研究 . . . . . . . . . . . . . . . . . . . . . . . 2
3 方法概述 . . . . . . . . . . . . . . . . . . . . . . . 2
4 論文貢獻 . . . . . . . . . . . . . . . . . . . . . . .  4
5論文架構. . . . . . . . . . . . . . . . . . . . . . . .  5
多角度人臉辨識研究 . . . . . . . . . . . . . . . . . . . .  6
1 以二維技巧的多角度辨識方法. . . . . . . . . . . . . . . 6
1.1 Tied Factor Analysis(TFA) . . . . . . . . . . . . . .6
1.2 Eigen Light-Fileds(ELF) . . . . . . . . . . . . . . 7
2 以三維模型為基礎的多角度辨識方法. . . . . . . . . . . . . .  8
2.1 Stereo Matching . . . . . . . . . . . . . . . . . .  8
2.2 3D Mprphable Model(3DMM) . . . . . . . . . . . . . . 8
2.3 Illumination Cone . . . . . . . . . . . . . . . . . 9
2.4 Symmetric Shape from Shading(SSFS)   .  .  .  .  .  10
2.5  Jiang’s Method  .  .  .  .  .  .  .  .  .  .  . .  11
2.6  Generic Elastic Models(GEMs)   .  .  .  .  .  . .  11
2.7  Heterogeneous Specular and Diﬀuse(HSD)   .  .  .  .11
三維人臉重建 .  .  .  .  .  .  .  .  .  .  .  .  .  .  . 13
1   人臉影像輻射度模型‹  .  .  .  .  .  .  .  .  .  .  .  13
2   參考模型參數計算   .  .   .  .  .  .  .  .  .  .  .  . 14
2.1    參考模型法向量計算—  .  .  .  .  .  .  .  .  .  .  14
2.2    參考模型反照率計算  .  .  .  .  .  .  .  .  .  .  .16
3   人臉三維模型重建   .  .  .  .  .  . .  .  .  .  .  .   16
3.1    估算光照係數 .  .  .  .  .  .  .  .  .  .  .  .  . 16
3.2    估算深度值.  .  .  .  .  .  .  .  .  .  .  .  .  17
3.3    估算反照率 .  .  .  .  .  .  .  .  .  .  .  .  .  18
 基於三維模型的多角度人臉辨識方法  .  .  .  .  .  .  .  .  . 19
1   人臉多角度樣本資料庫建立  .  .  .  .  .  .  .  .  .  .  19
2   測試樣本與資料庫樣本對齊.  .  .  .  .  .  .  .  .  .  . 21
3   Local Binary Pattern特徵與 Support Vector Machine在實驗中應用  22
 實驗與結果討論 .  .  .  .  .  .  .  .  .  .  .  .  .  . 24
1   實驗平台及主要參數  .  .  .   .  .  .  .  .  .  .  .24
2   實驗資料庫 .  .  .  .  .  .  .  .  .  .  .  .  .  . 24
3   實驗設置.  . .  .  .  .  .  .  .  .  .  .  .  .  .  25
4 三維人臉重建結果  .  .  .  .  .  .  .  .  .  .  .  . 26
5   多角度人臉辨識率.  .  .  .  .  .  .  .  .  .  .  .  .31
6   不同參考模型下的三維重建結果及多角度辨識.  .  .  .  .  . 33
7   結果分析討論 .  .  .  .  .  .  .  .  .  .  .  .  .  . 39
 實際系統製作.  .  .  .  .  .  .  .  .  .  .  .  .  .   41
1   系統平台.  .  .  .  .  .  .  .  .  .  .  .  .  .  .  41
2   系統流程 .  .  .  .  .  .  .  .  .  .  .  .  .  .   41
3  系統呈現.  .  .  .  .  .  .  .  .  .  .  .  .  .  41
 總結與未來方向.  .  .  .  .  .  .  .  .  .  .  .  . 43
參考文獻.  .  .  .  .  .  .  .  .  .  .  .  .  .  .  .  45

                                

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