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研究生:張哲豪
研究生(外文):Che-HaoChang
論文名稱:基於掃描線之串聯級分類器人臉偵測演算法開發及積體電路架構設計
論文名稱(外文):Scanline Based VLSI ArchitectureDesign of Face DetectionUsing Cascade of Classifiers
指導教授:謝明得謝明得引用關係
指導教授(外文):Ming-Der Shieh
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:2012
畢業學年度:100
語文別:英文
論文頁數:67
中文關鍵詞:人臉偵測串聯級分類器
外文關鍵詞:Face DetectionCascade of Classifiers
相關次數:
  • 被引用被引用:0
  • 點閱點閱:126
  • 評分評分:
  • 下載下載:9
  • 收藏至我的研究室書目清單書目收藏:0
人臉偵測研究主題在近十年一直有持續的進步,然而隨著攜帶式電子產品普及化,高效率且低複雜度的人臉偵測硬體實現需求變得越來越迫切。在人臉偵測演算法中,串聯級分類器(cascade of classifiers)已被證實是兼具速度和準確率之物件偵測演算法,然而卻少有文獻能將串聯級分類器加以實現成高效率的超大型積體電路(very-large-scale integration)設計。為了改善現存人臉偵測硬體電路之成本效益不彰問題,本篇論文首次整合顏色特徵和串聯級分類器,提出一基於掃描線之人臉偵測硬體電路架構。藉由結合膚色檢測機制,可以大幅減少 (i) 整張影像的搜尋計算時間 (ii)人臉分類所需之特徵數目。另外我們對輸入影像訊號採取動態取樣(dynamic down-sampling)進而克服人臉在不同影像中尺度變換之問題。與傳統硬體實現結果相比,我們所提出的硬體架構在處理160×120的彩色影像中,可以節省96%的記憶體存取時間並只需7%的分類特徵數目。實驗結果顯示,我們所提出的低硬體成本之電路架構可以實現快速且準確的人臉偵測功能。
Over the past decade, many advances have been made in the area of face detection. With the increasing usage of portable consumer devices, the low cost and high performance requirements are becoming more critical to face detection. Cascade of classifiers has been approved as one of the most accurate and high speed object detection methods. However, there are few researches which attempt to develop an effective VLSI architecture design of cascade of classifiers. In this thesis, a scanline based VLSI design is proposed to implement a cost-effective face detection system. This work presents a hybrid color feature and cascade of classifiers face detection algorithm. Using the skin color to recognize the region for the following face classification can dramatically reduce (i) the search range of the whole image (ii) the feature number for classification. We proposed a dynamic down-sampling framework which can merge the face segment between scanlines and overcome the scale variation problem between faces. The proposed work successfully save 96% frame memory access time and use only 7% of the classified features in processing 160×120 color images. Experimental result shows that the proposed scheme can achieve not only high processing speed and detection accuracy but also low hardware cost.
摘   要 iii
ABSTRACT iv
誌  謝 v
CONTENTS vi
LIST OF TABLES viii
LIST OF FIGURES ix
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Thesis organization 2
Chapter 2 Background 3
2.1 Face detection methods 3
2.1.1 Feature invariant approach 3
2.1.2 Template matching method 5
2.1.3 Appearance-based method 6
2.2 Boosted cascade face detection 7
2.2.1 Haar features 7
2.2.2 Integral image 8
2.2.3 AdaBoost algorithm 10
2.2.4 Cascade of classifiers 13
2.3 Traditional frame memory based architecture 14
Chapter 3 Proposed face detection algorithm 17
3.1 Skin color detection using hybrid color spaces 17
3.2 Efficient data scheduling for sub-image extraction 22
3.2.1 Robust face segment merging using midpoint tracking method 22
3.2.2 Flexible data retrieval by dynamic down-sampling 26
3.3 Face classification and localization 31
Chapter 4 Hardware architecture design and implementation 36
4.1 Hardware architecture 36
4.1.1 System architecture 37
4.1.2 Skin color decision and horizontal midpoint calculation unit 38
4.1.3 High-accuracy dynamic down-sampling approach 41
4.1.4 Low memory access design of address generator 43
4.1.5 High-throughput systolic architecture for fast integral image generation 48
4.1.6 Parallel classifier and rectangular descriptor 53
4.2 Design and verification flow 55
4.3 Experimental results and performance comparison 57
Chapter 5 Conclusion and future work 61
5.1 Conclusion 61
5.2 Future work 61
Reference 63


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