基於語意分割預測深度影像以幫助三維人臉辨識

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研究生：	程薰瑩 Cheng, Hsun-Ying
論文名稱：	基於語意分割預測深度影像以幫助三維人臉辨識 Segmentation-Aided Depth Map Estimation for RGB-D Face Recognition
指導教授：	賴尚宏 Lai, Shang-Hong
口試委員:	林嘉文 Lin, Chia-Wen 邱瀞德 Chiu, Ching-Te 林彥宇 Lin, Yen-Yu
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊工程學系 Computer Science
論文出版年：	2020
畢業學年度：	108
語文別：	英文
論文頁數：	43
中文關鍵詞：	3D人臉辨識、深度學習、電腦視覺、生成對抗網路
外文關鍵詞：	3D Face Recognition, Deep Learning, Computer Vision, Generative Adversarial Network
相關次數：	點閱：3 下載：0
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近年來由於深度學習的興起，在眾多領域都有相當飛速的進步，這之中也包含了人臉辨識。頂尖的二維人臉辨識已能辨識生活中大部分的人臉，然而在側臉或是角度變化大的情況下，辨識準確率往往會有大幅度的降低。另一方面，三維人臉辨識由於多了深度資訊，對於側臉的辨識率較二維人臉辨識高，但因為當前三維人臉的資料量過於缺乏，使得難以訓練出一個強大的模型。在本論文中，我們提出了一個可以將RGB影像轉為深度影像的生成對抗網路，稱作DepthGAN，來幫助提升在角度變化大的人臉辨識率。為了可以精準的分辨輸入人臉影像的五官位置以及人臉輪廓，我們還增加了一個語意分割的步驟，將語意分割的資訊和原本的RGB輸入影像合併在一起，來引導DepthGAN生成更加可信的深度影像資訊。接著我們將現有的大量RGB影像轉換成深度影像來實作三維人臉辨識，並實驗在三維及二維的測試資料上。實驗結果顯示，DepthGAN除了可以實現側臉的深度轉換，在表情的深度轉換上也比目前使用人臉重建產生的深度影像更加接近真實深度影像。人臉辨識的實驗結果也表明，用我們產生的RGB-D人臉資料訓練出來的模型，不論在二維或是三維資料上都能夠有效的提升人臉辨識準確率。

With the emergence of deep learning technology, we have witnessed rapid advances on many computer vision tasks, including face recognition. However, although 2D face recognition can achieve extremely high accuracy on frontal images, the accuracy is degraded for recognizing face images with large pose variations. 3D face recognition is considered to be robust under pose variation, but the numbers of subjects in public 3D face datasets are not large enough to train an accurate 3D face recognition model. In this paper, we propose a novel framework that estimates depth maps from RGB face images by including a semantic segmentation module for more accurate face region localization. With the segmentation mask, our depth generation module can produce more reliable depth estimation especially for faces with large pose angles. We produce a large-scale RGB-D dataset by converting VGGFace2 dataset to train a robust face recognition model. Our experiments demonstrate that the proposed method can provide more accurate depth estimation under large pose and expression variations. In addition, the proposed system improves face recognition accuracy for both 2D and 3D face recognition tasks on several public datasets.

Abstract
Contents
Introduction ---------- 1
1 Motivation ---------- 1
2 Problem Statement ---------- 3
3 Contributions ---------- 4
4 Thesis Organization ---------- 5
Related Work ---------- 6
1 Conventional 3D Face Recognition ---------- 6
2 Learning-based 3D Face Recognition ---------- 7
3 3D Data Augmentation ---------- 8
Method ---------- 10
1 Face Segmentation ---------- 10
2 DepthGAN ---------- 11
2.1 Generator ---------- 12
2.2 Discriminator ---------- 14
3 RGB-D Face Recognition ---------- 15
Data Preparation ---------- 18
Experimental Results ---------- 21
1 Implementation Details ---------- 21
2 Datasets ---------- 21
2.1 2D Face Datasets ---------- 22
2.2 3D Face Datasets ---------- 23
3 DepthGAN Results ---------- 24
4 Face Recognition Evaluation on 2D Datasets ---------- 29
5 Face Recognition Evaluation on 3D Datasets ---------- 31
6 Ablation Study ---------- 32
6.1 Effect of the Segmentation Mask ---------- 32
6.2 Generation of Segmentation Masks ---------- 33
6.3 RGB-D Fusion Method for RGB-D Face Recognition ---------- 34
7 Failure Case ---------- 36
Conclusions ---------- 39
References ---------- 40
                                

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