臺灣博碩士論文加值系統

摘 要

由於人臉偵測系統因其在安全驗證系統、信用卡驗證、醫學、檔案管理、視頻會議、銀行提款機、系統公安(罪犯識別等)等方面的廣泛的應用而越來越成為當前身份識別和人工智慧領域的一個熱門的研究議題。
這個議題雖然已經討論很多年了，也有許多人提出了許多不同的方法和研究，也得到了不錯的成果，而再加上最近因安全性上的議題廣泛的討論如何認證身份的問題，因此人臉偵測廣泛的被重視，並且廣泛地應用在各種領域中。在本論文中，我們提出了一個以高曲率特徵為基礎的人臉偵測的方法，首先我們利用正規化的方向通道及Teager能量組合，並搭配曲率模組，使得對於影像的邊和線的特徵能更加準確的偵測到，然後利用19�e19大小的mask來掃描整張影像，再計算每一個mask的像素總和值，找出可能的人臉候選區的高曲率特徵區域，最後再經由支援向量機器(Support Vector Machine)的確認及認證，找到人臉的區域位置。

Abstract

Identity recognition plays a fundamental role in applications such as security verification，credit card identification，medical services，archive management，ATM services，and public security systems，etc.
Face detection has become one of the active research subjects in identity recognition. The purpose of this thesis is to develop a face detection system based on high curvature features. High curvature edges and lines are accurately detected by applying the normalized orientation channels and curvature module. The density of the curvature features at a given region is measured by a size of 19�e19 mask. The likely face regions are those of the high density of the curvature features. The probable face regions are verified with a support vector machine that is trained with face and non-face database. Experimental results are compared with other face detection schemes.

目 錄
摘 要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
第一章 緒論 1
1-1 簡介 1
1-2 研究背景與動機 2
1-3 研究目標 4
1-4 章節說明 4
第二章　人臉偵測的相關文獻回顧 5
2-1 人臉偵測 5
2-2 人臉偵測的各種方法 6
2-2-1 以知識為基礎的人臉偵測方法 6
2-2-2 以特徵為基礎的人臉偵測方法 7
2-2-3 以樣板為基礎的人臉偵測方法 12
2-2-4 以表象為基礎的人臉偵測方法 14
第三章 人臉特徵的偵測和研究的方法 20
3-1 系統架構流程 24
3-2 正規化的方向通道 25
3-2-1 高斯微分濾波器 26
3-3-2 區域灰度值調整方式 28
3-3-3 Teager能量組合 30
3-3 曲率模組 34
3-4 特徵擷取方法 37
3-5 支援向量機器(Support Vector Machine；SVM) 43
第四章 系統實作及實驗結果 49
4-1 人臉訓練樣本正規化處理 49
4-2 人臉訓練樣本資料庫之訓練流程 50
4-3 人臉驗證流程 57
4-3-1 SSIP資料庫 58
4-3-2 MIT CBCL資料庫 62
4-4實驗結果 67
4-5 與其他系統比較 75
第五章 結論及未來的發展 83
參考文獻 86
作者簡介 90

參考文獻

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