基於環型對稱賈柏濾波器及SVM之人臉識別系統

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姓名

邱柏勝(Po-Sheng Chiu) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

基於環型對稱賈柏濾波器及SVM之人臉識別系統
(Face Recognition System Based on Circularly Symmetrical Gabor Filter and SVM)

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至系統瀏覽論文 ( 永不開放)

摘要(中)

本篇論文提出了一個實現基於賈伯濾波器(Gabor filter)以及支持向量機(support vector machine, 簡稱SVM)的人臉識別演算法。在所提出的方法中，使用環型對稱賈伯濾波器(circularly symmetrical Gabor Filter, 簡稱CSG filter)來取代傳統的賈伯濾波器並結合SVM分類器，應用在影像識別上。
基於傳統賈伯濾波器的人臉辨識系統，對於增進人臉影像變化的穩健性是具有良好的效能，然而卻還是存在著一些問題。在傳統的賈伯濾波器中，不存在旋轉的不變性(rotation invariant)以及其計算複雜度過高，而這些問題在應用於動態及時影像識別上，會成為很大的缺點。因此，本篇論文提出使用環型對稱賈伯轉換濾波器取代傳統的賈伯轉換並且以支持向量機對影像做訓練，測試系統的排外性、濾波器參數對辨識效率的影響，以達到增進人臉識別系統的效能。

摘要(英)

In this thesis, the face recognition algorithm for the realization of Gabor filter and the support vector machine (SVM) classifier is proposed. In the proposed algorithm, the circularly symmetrical Gabor Filter (SCG Filter) is used to replace the traditional Gabor filter and the SVM classifier is integrated in the application of image recognition.
While the face recognition algorithm based on the Gabor filter has good performance in improving robustness on face image variances, there are still some problems. The traditional Gabor filter has no rotation invariant and its calculation is too complicated. These problems will become an obstacle in application of image recognition. In this thesis, a CSG Filter is proposed to replace the traditional Gabor transform filter and the SVM is used to train images in face recognition. Also, we test the exclusivity of the system and the impact of filter parameters on identification efficiency to promote the effectiveness of the face recognition system.

關鍵字(中)

★ 影像處理
★ 人臉識別
★ 賈柏濾波器
★ 環型對稱賈柏濾波器
★ 支持向量機

關鍵字(英)

★ Image Processing
★ Face Recognition
★ Gabor Filter
★ Circularly Symmetrical Gabor Filter
★ Support Vector Machine

論文目次

第一章緒論 1
1.1 研究動機 1
1.2 相關研究 4
1.3 主要貢獻 7
1.4 論文架構 8
第二章人臉定位 9
2.1 膚色偵測(Skin-color Detection) 9
2.1.1色彩空間(Color Space) 9
2.1.2 膚色擷取(Skin-color Extraction) 11
2.1.3 二值化處理(Binarization) 12
2.2 形態學處理(Morphology) 13
2.2.1 膨脹(Dilation) 14
2.2.2 侵蝕(Erosion) 15
2.2.3 斷開(Opening) 16
2.2.4 閉合(Closing) 17
2.3 連通元件標記演算法(Connected Component Labeling Algorithm) 19
2.3.1元件標記(Component Labeling) 21
2.4直方圖等化(Histogram Equalization) 24
2.5雙線性內插法(Bilinear Interpolation) 25
第三章應用環型對稱賈伯濾波器之人臉辨識 27
3.1 影像特徵分析 27
3.1.1 空間域之特徵分析 28
3.1.2 頻域之特徵分析 29
3.2 賈伯濾波器與視覺模型 32
3.2.1賈伯濾波器(Gabor Filter) 32
3.2.2 環型對稱賈伯濾波器(Circularly Symmetrical Gabor Filter) 36
3.3 濾波器之特徵提取 39
3.3.1 二維離散疊加摺積 40
3.3.2 快速傅立葉摺積 41
第四章支持向量機 42
4.1統計學習以及樣本學習模型 42
4.2風險函數 43
4.2.1 經驗風險最小化原則 44
4.2.2結構風險最小化原則 45
4.2.3最佳化風險函數 47
4.3支持向量機 48
4.3.1最佳分類超平面(optimal separating hyperplane) 49
4.3.2 線性可分情形 51
4.3.3 線性不可分情形 52
4.4訓練人臉影像 55
4.4.1一對一訓練 55
4.4.2一對多訓練 56
第五章實驗結果與討論 59
5.1實驗機制 59
5.1.1 實驗設備 59
5.1.2系統架構 60
5.2實驗結果與比較 62
5.2.1 混和人臉資料庫實驗 64
5.2.2 隨選ORL及混和人臉資料庫中12人做測試 66
5.2.3 濾波器參數影響 67
5.3實驗結果與討論 69
第六章結論與未來展望 71
6.1結論 71
6.2未來展望 72

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指導教授

鍾鴻源(Hung-Yuan Chung)

審核日期

2015-8-19

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