對於三維人臉識別的資料擴充應用

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姓名

葉千瑋(Chien-Wei Yeh) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

對於三維人臉識別的資料擴充應用
(Data Augmentation for 3D Face Recognition)

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摘要(中)

人臉識別是近年來受關注的熱門科技之一，特別是在深度學習與硬體設備的幫助下，實用價值更提升、辨識精準度越高。其中，訓練資料集的數量與深度學習的準確度有高度的相關性，目前大部分知名的人臉識別模型都使用到百萬張以上的人臉影像作為訓練資料，此外資料品質、資料集的分布偏差也會影響模型學習的成效。然而，相較於二維人臉識別，深度學習在三維人臉識別的發展較受限，很大的原因在三維臉部資料集的缺乏。在此篇論文，我們嘗試使用針對三維人臉的資料擴充方法來提升三維人臉識別的穩健性。利用合成的大量虛擬三維人臉資料，我們在人臉表情、臉部角度做變化增加資料的多樣性，並且在實驗探討：使用虛擬合成的資料是否可以增加三維人臉識別的強健性？我們證實使用虛擬合成的人臉資料可以有效地幫助三維人臉識別系統。

摘要(英)

In recent years, deep learning has important increased the performance of 2D face recognition systems with the use of large-scale labeled image data. Deep neural networks can be closely approaching human-level depend heavily on the amount and quality of facial training data. However, contrast with 2D face recognition, training discriminative deep features for 3D face recognition is very difficult. Because of the unavailability of large training datasets, recognition accuracies have already saturated on existing 3D face datasets due to their small gallery sizes. Unlike 2D photograph, the collection of annotated high-quality large 3D facial scan datasets cannot be sourced from the web. In this paper, we show that using synthetically generated data as CNN training dataset can effectively work for 3D face recognition by fine-tuning the CNN with real-world data. We propose a 3D augmentation method for enlarging 3D facial data, we can generate 3D facial data with arbitrary amounts of facial identities, facial expression and pose variations by using 3D morphable face model. Finally, in our experiment we use two real-world 3D facial datasets to be compared. Our method outperforms the 3D face recognition system training only with real-world dataset. As well as, we find the significant accuracy improvement with the help from synthetic 3D facial data.

關鍵字(中)

★ 三維人臉識別
★ 三維人臉形變模型
★ 資料擴充
★ 合成資料
★ 三維人臉重建

關鍵字(英)

★ 3D Face Recognition
★ 3D Morphable Model
★ Data Augmentation
★ Data Synthesis
★ 3D Face Reconstruction

論文目次

中文摘要 I
Abstract II
圖目錄 III
表目錄 V
章節目次 VI
第一章緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 研究方法與章節概要 3
第二章相關研究 4
2.1 資料擴充 4
2.2 合成資料 6
2.3 三維人臉重建 8
2.3.1 通用人臉模型 8
2.3.2 三維人臉形變模型 9
2.3.3 基於深度學習的三維人臉重建 14
第三章深度學習與人臉識別相關研究 16
3.1 深度學習概論 16
3.1.1 類神經網路 17
3.1.2 深度學習 18
3.1.3 卷積神經網路 19
3.2 二維人臉識別 21
3.2.1 特徵臉 22
3.2.2 局部二值模式 23
3.2.3 DeepFace 23
3.2.4 FaceNet 24
3.3 三維人臉識別 26
第四章實驗架構 28
4.1 人臉深度圖合成器 29
4.1.1 人臉形狀 31
4.1.2 人臉表情 32
4.1.3 人臉三維角度變化 33
4.2 特徵向量學習器 36
4.3 實驗驗證 38
第五章實驗設計與實驗結果 40
5.1 電腦軟硬體配置 40
5.2 資料集說明 41
5.2.1 真實人臉資料集 41
5.2.2 虛擬合成人臉資料集 42
5.3 實驗設計 43
5.3.1 訓練參數 43
5.3.2 實驗度良方式 43
5.3.3 實驗基準：虛擬合成資料集與真實資料集的差異基準 44
5.4 實驗結果與比較 45
5.4.1 合成條件控制 45
5.4.2 真實資料集輔助 47
5.4.3 極端減少真實資料集輔助 48
5.4.4 增加合成個體 51
5.5 延伸實驗 52
第六章結論與未來研究方向 54
參考文獻 56

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指導教授

王家慶(Jia-Ching Wang)

審核日期

2019-8-20

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