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研究生:孫瑞強
研究生(外文):Rui-Qiang Sun
論文名稱:植基於突出特徵之人臉辨識
論文名稱(外文):A Conspicuous-Feature-based Face Recognition method
指導教授:廖斌毅潘正祥李建樹李建樹引用關係
指導教授(外文):B.Y.LiaoJ.S.PanC.S.Lee
學位類別:碩士
校院名稱:國立高雄應用科技大學
系所名稱:電子工程系
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:2009
畢業學年度:97
語文別:中文
論文頁數:70
中文關鍵詞:人臉辨識突出特徵五官分割
外文關鍵詞:Face RecognitionConspicuousFacial features division
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人臉辨識在這幾年受到高度的重視,現有辨識方法主要都是取整張臉的特徵為主, 例如PCA 、LDA,鮮少進一步去分析每人的五官特徵在資料庫裡是否有特別的代表性。本論文提出突出特徵的想法,找出每個人的五官在資料庫當中是否具有突出性,我們採用突出特徵,並利用Gabor Wavelet Filter(GWF)抗光影的特性來提取特徵,同時利用基因演算法(GA)進一步精簡GWF特徵,然後將這些突出特徵以加權值的方式輔助人臉辨識。研究結果顯示本方法只需要原本30%的資訊量,就可以比使用全部資訊量的方法之辨識率提高3.6%,而減少資訊量自然也減少了計算處理上花費的時間。
Face recognition got more high attention in these years, but current method for recognition always takes the whole face as the main feature, such as PCA and LDA. However, further analysis on the five senses of human face in database is not made in those methods in consideration of representation. The idea of conspicuous features is proposed in this paper and utilized for investigating the conspicuousness of five senses in each man. For example: The eye is a characteristic. In a crowd of big eyes person, somebody with small eyes manipulates the prominent characteristic. With this characteristic, this person could be recognized quickly according to this distinguishing feature. Based on this idea, we use the conspicuous features, and extract the characteristic using Gabor Wavelet Filter (GWF). Meanwhile, Genetic Algorithms method (GA) is used for simplifying the GWF characteristic. The reason for reducing the characteristic dimension is because GWF would amplify the characteristic 40 times, and then weighting on these conspicuous features is used for assisting face recognition.
目 錄

中文摘要 ------------------------------------------------------- i
英文摘要 ------------------------------------------------------- ii
誌謝 ------------------------------------------------------- iii
目錄 ------------------------------------------------------- iv
表目錄 ------------------------------------------------------- v
圖目錄 ------------------------------------------------------- vi

一、 緒論---------------------------------------------------- 1
1.1 研究目的---------------------------------------- 1
1.2 研究動機---------------------------------------- 2
1.3 研究背景---------------------------------------- 3
1.4 研究方法---------------------------------------- 4
1.5 論文架構---------------------------------------- 5

二、 目前常見人臉辨識法--------------------------------------- 6
2.1 PCA---------------------------------------------------- 6
2.2 LDA---------------------------------------------------- 15
2.3 WAVELET------------------------------------------------ 23

三、 植基於突出特徵之人臉辨識法-------------------------------- 30
3.1 辨識流程------------------------------------------------ 30
3.2 五官切割方法--------------------------------------------- 32
3.3 GABOR濾波器--------------------------------------------- 36
3.4 突出特徵------------------------------------------------ 42
3.5 基因演算法---------------------------------------------- 46

四、 實驗部份------------------------------------------------ 64
4.1 PCA、LDA與WAVELET的實驗結果------------------------------ 65
4.2 採用突出特徵的實驗結果------------------------------------ 66

五、 結論---------------------------------------------------- 67

六、 未來研究方向--------------------------------------------- 68

參考文獻 ------------------------------------------------------- 69
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[8] Dian Gong , Jianhua Lu, Qiong Yang and Xiaoou Tang, “ Extracting
Micro-Structural Gabor Features for Face Recognition, ” IEEE
International Conference on Volume 2, Issue , 11-14 Sept.
Page(s): II – 942-5, 2005.

[9] Guoqiang Wang and Zongying Ou, “Face Recognition Based on Image
Enhancement and Gabor Features, ” Volume 2, 0-0 0
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[10] Tie-Ming Su, Xu-Sheng Tang and Zong-Ying Ou, “Optimal GABOR
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[11] Xinshan Zhu, Xuetao Feng, Xiaoxu Zhou, Yong Gao and Yangsheng
Wang, “ Weighted Gabor Features in Unitary Space for Face
Recognition. Automatic Face and Gesture Recognition, ”
FGR 2006. 7th International Conference on Volume ,
April 2006 Page(s):6 pp. – 84, 2006.
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