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研究生:郭姵君
研究生(外文):Pei-Chun Kuo
論文名稱:人臉肌膚分析系統之設計與實作
論文名稱(外文):Facial Skin Analysis System Design and Implementation
指導教授:李錫捷李錫捷引用關係郭文嘉郭文嘉引用關係
指導教授(外文):Hsi-Chieh LeeWen-Jia Kuo
口試委員:謝瑞建林熙禎
口試委員(外文):Rui-Jian XieXi-Chen Lin
口試日期:2014-07-10
學位類別:碩士
校院名稱:元智大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:中文
論文頁數:49
中文關鍵詞:影像處理人臉偵測膚質特徵色彩空間
外文關鍵詞:Image processingFace DetectionSkin FeaturesColor Space
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現在醫療美容越來越受到青睞與重視,人們除了社交需要外也受到影音媒體耳濡目染下更重視自己的外觀,而偵測肌膚技術的重要性與需求性日益增加。現在醫療美容肌膚分析系統的價格較昂貴,非一般民眾能負擔,因此開發此系統透過影像處理方式對人臉肌膚進行有效判斷,可以降低價格昂貴因素。

本論文透過輸入影像中找到人臉位置,並且自動找到臉部特徵,人臉擷取 5個感興趣區域(ROI)來進行膚質檢測。最後使用四種不同的方法,分別從ROI 中偵測痘疤、痣、膚斑及皺紋。取得ROI區域後可以選取欲偵測部位進行偵測,處理方式針對影像中的色彩空間特性進行轉換,並進行執行膚斑、痘疤、皺紋進行膚質影像特徵值,之後利用敏感度、準確度、特殊性與Pearson 積差相關統計方法評估實驗結果。
The medical cosmetology has become increasingly important for people nowadays. Besides the manner of socializing, people care more about their appearance under the influence of social media. As a result, the technology of skin detection and dermatology evolves as well. But it is hardly affordable for people taking a analysis process by using the technology of medical cosmetology skin analysis system, which becomes a push power for inventing the system of analyzing human face skin by the technology of image processing.

In this paper, we invent a system that can automatically find and locate the features of faces and extract 5 regions of interests (ROI) to engage dermatology, and uses four different ways to detect smallpox, mole, skin spots and wrinkles. After selecting the ROI, the system will detect the skin features by transforming the RGB color space to collect the result. In the end we use Pearson Product Moment Correlation Coefficient and use sensitivity, specificity, accuracy to evaluate the experiment result.
書名頁 i
論文口試委員審定書 ii
授權書 iii
中文摘要 vi
英文摘要 vii
誌謝 viii
目錄 ix
表目錄 xi
圖目錄 xii
第一章、 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 1
1.3 研究流程 2
第二章、文獻探討 3
2.1 肌膚的構造 3
2.1.1 皺紋 4
2.1.2 痘疤 5
2.1.3 膚斑 6
2.1.4 痣 7
2.2 人臉偵測 7
2.2.1 積分影像 8
2.2.2 AdaBoost分類器 11
2.2.3 層疊分類器(Cascade Classifier) 11
2.3 色彩模型 12
2.3.1 RGB色彩模型空間 14
2.3.2 HSI 色彩模型空間 15
2.3.3 L*a*b 色彩空間 16
2.3.4 L Angle A Angle B色彩空間 17
2.3.5 YCbCr 色彩空間 18
2.4 邊緣偵測方法 19
2.4.1 Canny 19
2.4.2 Sobel 20
第三章、 研究架構與方法 21
3.1 研究架構 21
3.2 臉部偵測 21
3.3 皺紋偵測 22
3.4 膚斑偵測 24
3.5 痣偵測 26
3.6 痘疤偵測 29
第四章、 研究結果 32
4.1 實驗資料 32
4.2 開發環境 33
4.3 系統功能介紹 33
4.4 影像特徵值整理表 37
4.5 實驗結果 38
第五章、 結論與展望 48
參考文獻 49
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