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系統識別號 U0002-1707200522440100
中文論文名稱 以膚色分割及類神經網路為基礎之人臉偵測
英文論文名稱 Face Detection Based on Skin Color Segmentation and Neural Network
校院名稱 淡江大學
系所名稱(中) 資訊工程學系碩士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 93
學期 2
出版年 94
研究生中文姓名 王淑儀
研究生英文姓名 Shu-Yi Wang
學號 692192106
學位類別 碩士
語文別 中文
口試日期 2005-06-24
論文頁數 67頁
口試委員 指導教授-林慧珍
委員-林慧珍
委員-顏淑惠
委員-黃俊堯
中文關鍵字 人臉偵測  膚色分割  倒傳遞神經網路 
英文關鍵字 face detection  skin color segmentation  back-propagation neural network 
學科別分類 學科別應用科學資訊工程
中文摘要 人臉偵測是一項具挑戰性的工作,也是人臉追蹤與辨識系統之重要的前處理部份,本論文提出一套人臉偵測的方法。首先,利用膚色資訊快速地找出人臉可能存在之區域,並得到一膚色二值化圖(Skin Map),再對此膚色二值化圖做雜訊去除的處理以及型態學的運算(浸蝕、擴張與連通元件),並利用臉部的長寬比特性過濾出可能的人臉區塊。接著,對這些可能的人臉區塊做眼睛的偵測,若找到眼睛,則利用眼睛的位置預測人臉大小並框出可能的人臉範圍,即候選臉區塊,若找不到眼睛,則視為非人臉區塊。最後,利用類神經網路,針對候選臉區塊進行臉部驗證的工作。
實驗結果顯示,本文可以有效地偵測影像中的人臉,並可以克服影像中人臉不同亮度、大小、旋轉與多人人臉的問題。
英文摘要 This paper proposes a human face detection system based on skin color segmentation and neural networks. The system consists of several stages. First, the system searches for the regions where faces might exist by using skin color information and forms a so-called skin map. After performing noise removal and some morphological operations on the skin map, it utilizes the aspect ratio of a face to find out possible face blocks, and then eye detection is carried out within each possible face block. If an eye pair is detected in a possible face block, a region is cropped according to the location of the two eyes, which is called a face candidate; otherwise it is regarded as a non-face block. Finally, each of the face candidates is verified by a 3-layer back-propagation neural network. Experimental results show that the proposed system results in better performance than the other methods, in terms of correct detection rate and capacity of coping with the problems of lighting, scaling, rotation, and multiple faces.
論文目次 中文摘要 ........................................Ⅰ
ABSTRACT ........................................Ⅱ
目錄 ............................................Ⅲ
圖目錄 ..........................................Ⅵ
表目錄 ..........................................Ⅷ
第一章 緒論 ......................................1
1.1 研究動機與目的 ..........................1
1.2 系統流程 ................................2
1.3 章節組織 ................................4
第二章 人臉偵測相關研究 ..........................5
2.1 樣板比對法 ..............................6
2.2 以特徵為基礎法 ..........................8
2.3 以知識為基礎法 ..........................14
2.4 機器學習法 ..............................16
第三章 候選臉區塊之搜尋 ..........................18
3.1 可能的人臉區塊之判定 ....................18
3.1.1 膚色偵測 ...........................18
3.1.2 膚色分割 ...........................22
3.1.3 過濾出可能的人臉區塊 ...............24
3.2 擷取候選臉區塊 ..........................28
3.2.1 眼睛偵測與配對 .................28
(1) 眼睛偵測 ..................28
(2) 眼睛配對 ..................30
3.2.2 擷取候選臉區塊 .................31
第四章 候選臉區塊之驗證 ..........................33
4.1 倒傳遞神經網路簡介 ......................33
4.2 倒傳遞神經網路訓練 ......................37
4.2.1 參數設定 .......................37
4.2.2 樣本取得 .......................39
4.2.3 樣本正規化 .....................40
4.3 候選臉區塊驗證 ..........................42
第五章 實驗結果與分析 ............................44
5.1 倒傳遞神經網路效能評估 ..................44
5.2 人臉偵測系統實驗 .......................46
5.2.1 膚色偵測實驗 ...................48
5.2.2 膚色分割實驗 ...................49
5.2.3 可能的人臉區塊實驗 .............50
5.2.4 人臉偵測結果 ............................51
5.3 實驗結果分析.............................52
5.3.1 可處理之人臉偵測問題 ...........52
5.3.2 待解決之問題 ...................56
5.3.3 與其他系統比較 .................58
第六章 結論與未來研究方向.........................61
6.1 結論 ....................................61
6.2 未來研究方向 ............................62
參考文獻 .........................................63


圖 目 錄
圖1.1系統流程圖 .................................3
圖3.1 Chromatic Space中的膚色分佈圖 ..............19
圖3.2 高斯模型 .................................. 19
圖3.3 HSV色彩空間 ............................. 20
圖3.4 膚色偵測結果 .............................. 21
圖3.5 Skin map .................................. 23
圖3.6 膚色分割過程 .............................. 24
圖3.7 人臉直立狀態 .............................. 26
圖3.8 過濾出可能的人臉區塊 ...................... 26
圖3.9 眼睛偵測 .................................. 29
圖3.10 眼睛與臉部的幾何關係 ..................... 30
圖3.11 臉部模型 ................................. 31
圖3.12 圖3.9(a)對應的候選臉區塊 .................. 32
圖4.1 倒傳遞神經網路架構 ........................ 34
圖4.2 處理單元架構圖 ............................ 35
圖4.3 雙彎曲函數 ................................ 38
圖4.4 亮度正規化 ................................ 41
圖4.5 候選臉區塊驗證流程圖 ...................... 43
圖5.1 部份人臉與非人臉訓練樣本 .................. 44
圖5.2 程式介面 .................................. 47
圖5.3 單人與多人之膚色偵測結果 .................. 48
圖5.4 單人與多人之膚色分割結果 .................. 49
圖5.5 可能的人臉區塊 ............................ 50
圖5.6 單人與多人之人臉偵測結果................... 51
圖5.7 不同大小的人臉偵測結果 .................... 52
圖5.8 不同亮度的人臉偵測結果 .................... 52
圖5.9 黃種人之人臉偵測結果 ...................... 53
圖5.10 不同臉部表情的人臉偵測結果 ............... 53
圖5.11 不同臉部姿勢與旋轉角度的人臉偵測結果 ..... 54
圖5.12 臉部部份遮蔽的偵測結果 ................... 54
圖5.13 複雜背景下的人臉偵測結果 ................. 55
圖5.14 多人人臉偵測結果 ......................... 55
圖5.15 影像亮度太亮與太暗導致人臉偵測失敗 ....... 56
圖5.16 眼睛被頭髮遮住 ........................... 57
圖5.17 側臉偵測失敗 ............................. 57
圖5.18 Fröba與Küblbeck [30]中的人臉偵測結果 ......... 58
圖5.19 Fröba與Küblbeck錯誤偵測結果 .............. 60



表 目 錄
表2.1 各種人臉偵測方法之分類 .....................17
表5.1 倒傳遞神經網路分類結果 .....................45
表5.2 與Fröba及Küblbeck [30]系統比較 ...............59
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[29] The Champion dataset, http://www.libfind.unl.edu/alumni/events/champions.
[30] Bernhard Fröba and Christian Küblbeck, "Face Detection and Tracking using Edge Orientation Information", SPIE Visual Communications and Image Processing, (2001), pp.583-594
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