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研究生:郭俊麟
研究生(外文):Jiun-Lin Guo
論文名稱:以Graph cut演算法為基礎的連續影像人臉偵測系統
論文名稱(外文):A Video Face Detection Method Using the Graph Cut Algorithm
指導教授:方瓊瑤方瓊瑤引用關係
指導教授(外文):Chiung-Yao Fang
學位類別:碩士
校院名稱:國立臺灣師範大學
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:2012
畢業學年度:101
語文別:中文
論文頁數:96
中文關鍵詞:圖分割膚色偵測人臉偵測教室前景擷取
外文關鍵詞:graph cutskin color detectionface detectionclassroombackground subtraction
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本論文提出一套可在各種教室情境中運作的人臉偵測系統,偵測對象為教室中的多名學生,主要應用在自動教室觀察與記錄系統中。本研究採用顏色做為人臉偵測時的特徵,且利用graph cut技術做為人臉偵測時主要的方法。
以顏色為特徵的人臉偵測有著較不受頭部轉動和傾斜影響的優點,因為在頭部轉動和傾斜時,膚色依然在人臉中佔有一定比例的面積;至於眼睛、嘴巴和鼻子等其他人臉特徵在頭部轉動和傾斜時(尤其是轉動)在影像中較不穩定。這個顏色特徵的優點對於在教室中進行自動人臉偵測來觀察與記錄學生的行為有很大的幫助,因為在課堂中學生頭部的姿勢變化常常都是有意義的,如疲憊時打瞌睡、表示贊同時點頭或心不在焉時將人臉轉向他處等,而這些變化也往往是教學觀察者們(教師、研究人員)所關心的現象。因此,本系統若能夠在各種頭部姿勢狀態下做人臉偵測,就能夠更進一步地去分析這些姿勢變化和其所代表的意義。
利用顏色特徵來偵測人臉必須選擇一個適當的色彩空間,並且決定人臉的膚色在該色彩空間中的範圍。然而,這類作法常會遇到兩個問題,一是不同的光線以及人種需定義不同的膚色範圍,二是在教室中有許多物體顏色接近膚色(如原木色課桌椅),會降低人臉偵測的正確率。針對第一個問題,本研究提出一個動態的膚色範圍定義方式;而為了解決第二個問題,本研究提出一個穩定的方法在影像中擷取前景(即學生的部分)。此方法結合單點建模與graph cut的技術,可以得到完整不破碎的前景,在前景的範圍內擷取膚色,避免類膚色背景的干擾。
另一方面,利用膚色在Hue色彩空間中高度集中的特質,本研究再次以graph cut技術優化膚色區域的偵測結果,統計收集到的膚色像素、動態更新膚色範圍,以提高偵測的穩定性。
在實驗時,本研究架設單一攝影機來擷取影像,每張影像中均包含4~6位學生。本研究假設初始教室沒有學生,系統首先進行背景建置,待學生進入教室,系統偵測到影像中有前景出現後,便會開始進行人臉偵測。實驗結果顯示,本研究提出的人臉偵測技術,較不受各種頭部轉動和傾斜角度之影響,並且能夠在低解析度影像下,維持高準確率。

We propose a face detection system which is used in classrooms with various environments. The targets are several students in class, whose behaviors are to be observed and recorded in a classroom observation system. The feature chosen for detection is “color”, and the kernel method is the well-known graph cut algorithm.
Color feature is robust against head pose changing because the area of skin regions changes little during head rotating or tilting, while other features like eyes, noses and mouth are unstable under these conditions. This character is useful for observing students’ behaviors in class since it’s usually meaningful when a student change his head pose. For example, one may doze off if he is tired, nod his head to show his agreement, or turn his head out when he distracted. These behaviors are also important events which educational researchers concern. As a result, if we can perform face detection under various head poses, then the results can be used to detect such behaviors mentioned above for further researches.
To detect faces with color feature, we must choose a proper color space first, and determine the range of “skin color”. However, this kind of methods have two problems. First, the range changes with different lighting conditions and human races. Second, there are many non-human object with skin like color which affects on the precision a lot. For the first problem, we propose a dynamic learning scheme to change the skin color range frame by frame. And to solve the second problem, we propose a robust background subtraction method to eliminate non-human object. This method combining pixel based background modeling and the graph cut algorithm extracts complete foreground region from the input frame and thus avoid the effect of skin like background pixels.
On the other side, since the hue values of skin color pixels are distributed concentratedly in hue color band, we apply graph cut to improve the result of skin color detection, and then collect the skin pixels for learning new skin color range.
In the experiments, we set up a single camera, and there are 4~6 students in the image. Assuming an empty classroom in the beginning, the system constructs the background model first. Then, when objects appear in the image, the system will start to perform face detection. According to the experimental result, the technique proposed is robust under various head poses, and retain high precision in the low resolution images.

摘要 I
Abstract III
誌謝 V
目錄 VI
圖目錄 VIII
表目錄 XII
第一章 緒論 1
1.1 研究背景與目的 1
1.2 文獻探討 5
1.2.1前景擷取方法 5
1.2.2 Graph cut 演算法 10
1.2.3 膚色偵測 11
1.3 系統架構 15
第二章 影像前處理 18
2.1 差值影像 18
2.2 對difference histogram前景擷取演算法的改良 22
第三章 Graph cut前景擷取技術 26
3.1 Graph cut技術 26
3.1.1 名詞定義 26
3.1.2 影像標記(labeling)和成本函數(cost function) 27
3.1.3 最小化成本函數 29
3.1.4 相關定理證明 31
3.2 應用於影片前景擷取 33
第四章 Dynamic Graph Cut 38
4.1 Max-flow min-cut演算法的時間複雜度 39
4.1.1 Ford-Fulkerson與residual graph 39
4.1.2 Graph cut和maximum flow演算法 41
4.2 Dynamic graph cut 43
4.2.1 增加新的hard constraint 43
4.2.2 Graph再參數化(reparameterization) 45
第五章 膚色區域偵測與人臉區域選取 51
5.1 膚色區域偵測 51
5.2 人臉區域選取 57
第六章 實驗結果與討論 60
6.1 去背景實驗結果 60
圖6.18 去背景失敗之例子 71
6.2 膚色區域偵測實驗結果 71
6.3 人臉區域選取之結果 81
第七章 結論與未來工作 88
7.1 結論 88
7.2 未來工作 89
參考文獻 90

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