臺灣博碩士論文加值系統

人臉身分辨識是人臉辨識中的一項分支，可以做為人臉辨識的前處理或是結合其他辨識來提升辨識效果。此外，人臉辨識相關技術不僅僅能用於身分辨識上，甚至在影像及多媒體的相關應用上都能看到其蹤跡。
臉部辨識中人臉身分辨識是一項重要且熱門的研究主題與方向，並且有著許多的方面的應用。在本篇論文中，建構在一維空間獨立類別特徵分析之一維相關性濾波器將被應用在人臉身分辨識。相較於原本建構在二維空間獨立類別特徵分析，一維空間獨立類別特徵分析將影像資訊轉為向量，並加入建構於核主成份分析特徵空間上的最佳化相關濾波器，是一套完整且有效的辨識方法。
我們將於本篇論文中提出一套基於核主成份分析的相關濾波器模組，稱為特徵濾波器。
在論文中系統架構可分為三個部分：（1）前處理模組、（2）訓練模組以及（3）測試模組。
實驗結果顯示，核主成份分析（KPCA）搭配最佳化相關濾波器（OTF）可得到最佳性能88.2%。

Face verification is a branch of face recognition can be used as pre-treatment or in combination with other identification to improve recognition results. In addition, the human face recognition technology not only can be used for identity, even in the images and related multimedia applications can see it.
Face verification from face images has many applications and is thus an important research topic. In this paper, a one-dimensional correlation filter based class-dependence feature analysis（1D-CFA） method is presented for face verification. Compared with original CFA that works in the two dimensional（2D）image space, 1D-CFA encodes the image data as vectors. In 1D-CFA, a new correlation filter called optimal trade-off filter（OTF）, which is designed in the low-dimensional kernel principal component analysis（KPCA）subspace, is proposed for effective feature extraction.
We will discuss a new correlation filter module called the eigen filter that designed in the kernel principal component analysis（KPCA）subspace. In the thesis, the system structure can be divided into three parts：（1）preprocessing module,（2）Training module and（3）Test module. The experimental results show that the best performance of 88.2% is achieved with the kernel principal component analysis （KPCA） and optimal trade-off filter（OTF）.

誌謝 I
摘要 II
ABSTRACT III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章 概述 1
1.1 研究動機 1
1.2 研究目標與方向 3
1.3 論文大綱與組織 4
第二章 人臉辨識相關技術之文獻探討 5
2.1 人臉偵測文獻回顧 5
2.1.1. 重要人臉偵測技術 7
2.1.2. 其它常見人臉偵測技術 10
2.2 人臉辨識文獻回顧 11
第三章 主成份分析與相關濾波器 13
3.1 Principal Component Analysis（PCA） 13
3.2 Kernel Principal Component Analysis（KPCA） 16
3.3 Fourier Transform 18
3.4 Correlation Filter 20
3.4.1. Minimum Average Correlation Energy Filter 22
3.4.2. Minimum Variance Synthetic Discriminant Function Filter 23
3.4.3. Optimal Trade-off Filter 24
3.4.4. Peak-to-sidelobe Ratio 24
第四章 人臉辨識系統 26
4.1 Kumar提出之人臉辨識系統 26
4.2 前處理模組 28
4.3 Kumar系統架構之改良 28
4.3 Eigen Filter系統架構 30
第五章 實驗成果 33
5.1 實驗環境 33
5.2 資料庫 33
5.3 系統結構比較 34
5.4 實驗結果 35
第六章 結論 40
參考文獻 41

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