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研究生:黃啟清
論文名稱:基於姿勢感知之深度卷積網路的人臉特徵點偵測
論文名稱(外文):Facial Landmark Detection using Pose-Aware Deep Convolutional Network
指導教授:許秋婷許秋婷引用關係
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:38
中文關鍵詞:人臉特徵點偵測深度學習卷積神經網路
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人臉特徵點偵測通常會受到各種環境的影響,例如人臉姿勢的不同以及光影變化。我們觀察到人臉姿勢變化是一項影響人臉特徵點偵測準確率的一大因素。為了解決人臉姿勢對偵測準確率的影響,我們利用深度學習的方式去學習一個良好的迴歸器,並且提出了基於姿勢感知的卷積網路來解決姿勢變化的問題。我們首先提出了一個基於卷積網路的分類器來對人臉影像做姿勢的分類,之後我們提出了兩個卷積網路分別對應人臉的不同姿勢來偵測人臉特徵點。此外,我們利用了輪廓的限制來修改修正層。實驗結果驗證了姿勢感知的偵測器可以比原來的偵測器達到更好的效果。
Facial landmark detection usually suffers from the influence by the change of environment, such as pose variation and illumination. We observe that high pose variation is the one most influence the detection accuracy. To tackle the problem of pose variation, we adopt deep learning approach to learn a good regressor and propose a pose-aware CNN to tackle the pose variation. We first develop CNN classifier to classify facial image according to the pose. Next, we develop two CNN to detect the facial landmarks according to the corresponding pose. In addition, we adjust the refinement level by concluding the shape constraint. Our experimental results show that the pose-aware detector performs better than the original landmark detector.
中文摘要 1
Abstract 2
1. Introduction 4
2. Related work 6
2.1. Model-based approach 6
2.2. Detector-based approach 8
2.3. Regression-based approach 10
2.4. Deep learning-based approach 11
3. Proposed method 14
3.1. Preliminary work and discussion 14
3.2. Motivation 16
3.3. Pose-Aware Deep Convolutional Neural Networks 17
3.3.1. Structure 17
3.3.2. CNN for pose classification 18
3.3.3. Pose-aware CNN for facial landmark detection 19
3.3.4. CNN for refinement 22
3.4. Implementation detail 24
3.4.1. The structure in convolutional neural network 25
3.4.2. CNN training 26
4. Experimental results 30
4.1. Experimental setting 30
4.2. Pose classification result 31
4.3. Facial landmark detection result 31
5. Conclusions 36
6. Reference 37

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