人臉之偵測與線上辨識系統之設計與實作｜國立清華大學博碩士論文庫

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研究生：	張書豪 Shu-Huo Chang
論文名稱：	人臉之偵測與線上辨識系統之設計與實作 On the design and implementation of facial recognition
指導教授：	陳建祥 Jian-Shiang Chen
口試委員:
學位類別：	碩士 Master
系所名稱：	工學院 - 動力機械工程學系 Department of Power Mechanical Engineering
論文出版年：	2006
畢業學年度：	94
語文別：	中文
論文頁數：	66
中文關鍵詞：	人臉偵測、人臉辨識、色彩分割、歐式距離、線性鑑別分析
相關次數：	點閱：3 下載：0
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在本論文中，發展出一套包含人臉偵測、人臉特徵點定位與人臉辨識的實驗室成員辨識系統。人臉偵測，是利用膚色範圍找出可能的人臉區域，達到人臉初步定位的目的。另外，對於人臉影像，特徵點的擷取很容易受到光線、角度等外在因素影響，因此運用可靠的人臉特徵定位演算法，精確地將人臉五官特徵點定位。
接著，我們更進一步將這些特徵點轉為特徵向量以進行比對，即是利用各特徵向量間的相對關係，如距離、面積等，運用歐式距離演算法，將未知人臉影像與資料庫中資料比對，得到辨識結果。接著，我們修正歐式距離之算式，可以獲得更高的辨識率。另外，我們也使用線性鑑別分析法進行資料分類，最後，結合修正歐式距離之算式與線性鑑別分析法，針對特定團體成員進行辨識，可以獲得高於90%的辨識率。

A facial recognition system for designated members which including based on detection, facial feature points location and facial recognition were developed. Robust feature location algorithms were adapted to discreminate facial features location precisely, the feature points were the transformed into feature vectors and then compared with database. In other words, to correlate each feature vector, e.g. distance, area… etc, comparison of the data of unknown facial image with database using Euclidean distance algorithm was adopted, and recognition results can be obtained. To achieve a higher recognition rate, modified Euclidean distance was proposed and implemented. Furthermore, Linear Discriminant Analysis Method was adopted to classify the data more accurately. Finally, we combined the modified Euclidean distance and Linear Discriminant Analysis method in the recognition process. A better than 90% recognition rate for specific group of people can be achieved through experiments.

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...........................................1
1-2  文獻回顧...........................................2
1-3  論文架構...........................................4
第二章  人臉偵測與辨識..................................6
   2-1  人臉偵測........................................6
   2-2  色彩空間與顏色分割..............................6
   2-3  人臉辨識.......................................10
   2-4  影像前置處理...................................12
     2-4-1 影像大小正規化..............................12
     2-4-2 灰階正規化..................................13
   2-5  人臉特徵影像處理...............................14
     2-5-1  眼球特徵估測...............................14
     2-5-2  二值化影像.................................17
     2-5-3  Sobel邊緣偵測影像..........................18
     2-5-4  投影直方圖.................................19
   2-6  人臉特徵比對...................................20
     2-6-1  特徵向量...................................20
     2-6-2  歐氏距離...................................20
     2-6-3  線性鑑別分析...............................21
   2-7  軟體環境.......................................23
   2-8  結語...........................................23
第三章  實驗系統架構與人臉特徵定位.....................24
   3-1  實驗系統架構...................................24
   3-2  系統流程.......................................25
   3-3  人臉膚色偵測...................................26
     3-3-1  正規化RGB..................................26
     3-3-2  雜訊去除...................................28
     3-3-3  相鄰元素標定...............................30
   3-4  人臉特徵定位...................................32
     3-4-1  影像正規化與光線補償.......................33
     3-4-2  眼球特徵估測...............................34
     3-4-3  眼角與眼球特徵定位.........................36
     3-4-4  眉心特徵定位...............................40
     3-4-5  嘴角特徵定位...............................41
     3-4-6  鼻孔特徵定位...............................42
   3-5  人臉特徵定位結果...............................44
   3-6  結論...........................................44
第四章  人臉辨識結果與討論.............................45
   4-1  特徵向量選取...................................45
   4-2  人臉辨識結果...................................50
     4-2-1  歐式距離比對...............................50
     4-2-2  線性鑑別分析資料分類.......................53
     4-2-3  結合修正歐式距離與與線性鑑別分析之辨識法...54
   4-3  實驗結果討論...................................56
第五章  本文貢獻與結論.................................59
   5-1  本文貢獻.......................................59
   5-2  結論與未來研究之方向與建議.....................60
     5-2-1  結論.......................................60
     5-2-2  未來研究方向與建議.........................61
參考文獻...............................................62

                                

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