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研究生:嚴逸緯
研究生(外文):Yi-wei Yan
論文名稱:結合人形特徵偵測與幾何分析應用於人臉姿態估測與手勢辨識
論文名稱(外文):Integration of Human Feature Detection and Geometry Analysis for Real-time Face Pose Estimation and Gesture Recognition
指導教授:王駿發
指導教授(外文):Jhing-Fa Wang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
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:2009
畢業學年度:97
語文別:英文
論文頁數:57
中文關鍵詞:人臉姿態估測幾何分析人形特徵偵測手勢辨識
外文關鍵詞:gesture recognitionhuman feature detectiongeometry analysisface pose estimation
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近年來,數位產品漸漸走入生活,在不同層面的智慧型人機介面之需求漸漸提高,使得各種與人臉處理相關的技術研究迅速地成長。人臉偵測、人臉辨識相關技術等系統已廣泛地應用於身份辨識、門禁監控管理與人機介面等領域。
在人臉相關的研究領域裡,應用於人機界面的人臉姿態估計技術中是一個非常熱門的題目。在本篇論文中,我們以人臉不同的角度做為分類,將3D空間中的人臉姿態分類到不同的類別,並提出了一個以物件偵測為基礎並結合幾何分析的人臉姿態估計系統,主要包括人臉偵測與姿態估計兩大程序。這個方法快速且不只考慮系統上的效能,也一併考慮到了系統之後的擴展性。我們將系統規劃成模組化的架構,這將對之後系統的進階開發有很大的幫助。在我們所提出的系統中,是藉由不同的人形特徵偵測,例如:人眼偵測、頭與肩膀偵測、正面臉偵測、側面臉偵測等,設定了9個對應的人臉,並為所使用的偵測器定義了偵測器陣列。由於快速的物件偵測技術,在低解析度下(320*240)的人形特徵可以準確的被系統偵測到。而在偵測器陣列設計的改善方面,我們設計了層級的偵測器陣列,目的為偵測畫面中感興趣的區域,由此種層級偵測器的方式,系統效能得到改善並可以即時有效地偵測出畫面中的人臉姿態。
在手勢辨識方面,我們以物件偵測的方式為基礎,再加上輸入影像的前處理,設計了一個手勢辨識系統,目前設計了兩種手勢方便做家電控制,在最後實驗結果中,我們用幾個影片來實際測試這個系統的偵測效率,並結合手勢辨識系統後再直接應用於《成大優質數位生活體驗屋》內做家電控制。它不但可以有效的偵測畫面上的人臉位置,還可以準確的提供人臉的方向,並依照所偵測的手勢內容對家電下達指令。這個研究主要是提出一個應用人臉姿態估計的方法,以後若再加上人臉姿態的訓練模型預訓練,則可以更準確的增加偵測的準確性,也就會是一個更完整的偵測方法。
In recent years, the digital products have become more accessible to people. The requirement of the different levels of intelligent human-machine interface is increased gradually and conduces to grow more and more related research of human face technique. Face detection and face recognition technology applications such as identifications system, access control monitoring systems. Human-Computer Interactions (HCI) is more and more common in real life.
The survey of human face pose estimation, a human face-related research field is a popular topic in HCI. In this paper, we divide the human faces into several viewpoint categories according to their poses in 3D and propose a system to estimate human face pose based on object detection and geometry analysis. The system architecture includes two components: 1) Face detection, 2) Face Pose estimation. It is not only considered about performance, but also the extension of the system by using the modular structure design. We define 9-posed in this system by the human features detection such as eyes, head and shoulders, frontal face and profile face and we defined a detect array for these detectors. Because of the fast object detection algorithm, the features can be detected and get good detect rate in low resolution 320*240. To improve the detect array of this system, we design a cascade detector array which detect only the interested region in image and can detect 9 face poses in real-time. We can speed up the detection system by using the cascade detector array.
We have proposed a gesture detection system based on Paul and Viola’s object detection, and combine it with image processing to recognize the defined gesture. We define two gestures in gesture detection to control the appliance. In final chapter of this thesis, we will show the experimental results using the test videos we took. Then we combine the pose estimation system and gesture detection system and apply it to the appliance control in NCKU Aspire Home. The proposed system can not only detect the human pose’s position and pose effectively in image, but also order the appliance. In this research, we proposed a human face pose estimation system. If we add the face model with a pre-training mode, we can increase the system detect rate and it will be a complete detection approach.
中文摘要 I
Abstract III
誌謝 V
Contents VI
Figure List IX
Table List XI
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 The Trend of Face Pose Estimation 2
1.3 The Criteria as a Guide for Face Pose Estimation 3
1.4 Thesis Objective 4
1.5 Thesis Organization 4
Chapter 2 Related Works 5
2.1 Introduction to Digital Image 5
2.2 The Survey of Face Detection 6
2.2.1 Knowledge-Based Approaches 6
2.2.2 Feature Invariant Approaches 6
2.2.3 Template Matching Methods 6
2.2.4 Appearance-Based Methods 7
2.3 Some Other Approaches and Summary on Pose Estimation 8
2.3.1 Geometric Methods 8
2.3.2 Detectors Array 9
2.3.3 Hybrid Methods 9
Chapter 3 Human Face Detection and Geometry Analysis 11
3.1 Human Feature Detection 11
3.1.1 Integral Image 11
3.1.2 Rectangle Feature 14
3.1.3 Adaboost Algorithm 17
3.1.4 Cascade classification 20
3.1.5 The Summary of Human Feature Detection 23
3.2 Geometry Analysis on Pose Estimation 25
Chapter 4 Proposed Face Pose Estimation System and Algorithm 28
4.1 The Used Human Feature Detections in Our System 28
4.1.1 Detector array 29
4.2 The Overall Pose Estimation Architecture 30
4.2.1 Procedure A 31
4.2.2 Procedure B 33
4.2.3 Procedure C 34
4.3 Gesture Recognition 35
4.3.1 Related Works of Gesture Recognition 35
4.3.2 Gesture Recognition Based on Cascade AdaBoost Algorithm 36
4.3.3 Dataset of Training Step 36
4.3.4 Experimental Results 39
Chapter 5 Experimental Results and System Application 43
5.1 Experimental Results 43
5.1.1 The Detection Rate of Each Detector 43
5.1.2 The Detection Rate of Face Pose Estimation 45
5.2 The Comparison with Other Papers 47
5.2.1 Functional Comparison 47
5.2.2 Comparison with Each Work 48
5.3 System Applications in Appliance Control 49
5.3.1 The Experimental Equipments 49
5.3.2 Application Environment 50
5.3.3 The system flowchart 51
Chapter 6 Conclusions and Future Works 52
Chapter 7 References 53
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