臺灣博碩士論文加值系統

人臉辨識的研究相當普遍，由於人臉資料取得方便，而且可以廣泛運用在各種類型的控制系統中，提升其系統安全性。在過去的研究中，高辨識率往往會導致運算時間冗長，而縮短運算時間卻又得不到良好的辨識效果。因此需要快速找出具備代表性的特徵值，以提升辨識效能。
本研究提出一個簡單、快速且高辨識率的人臉辨識系統。此系統利用彩色影像，結合簡單的影像處理技術，如色彩轉換、邊緣偵測和像素投影，從影像中擷取人臉區域，並計算出該區域中具有代表性的特徵值；最後透過SimNet類神經網路，以計算權重的方式，對特徵資料做群組分類。其目的在於降低運算複雜度和縮短運算時間。
本實驗選用三十位受測者共150張影像進行實驗，人臉偵測的偵測率達到98.67%，特徵擷取的正確率達到91.33%，群組分類的平均辨識率達到90.60%。

The study of face recognition is quite common because human face images can be obtained conveniently and it can be used in various types of control systems to improve the system security. Typically, high recognition rate often resulted in long computing time and reducing computing time usually sacrificed the results of recognition rate. Therefore, it is necessary to quickly select the most representative feature values in order to increase the efficiency of recognition.
This study proposes a simple and fast face recognition system with high recognition rate. This system uses color images to combine with simple image processing techniques such as color conversion, edge detection and pixels projection. The system then detects the faces from all images and calculates the most representative feature values in the area. Finally, the system uses the SimNet neural network to divide the feature groups and classify them by calculating the weights of feature information. The goals of the present study are to reduce the computing complexity and shorten the computing time.
This study first collects 150 images from 30 different subjects to go through the entire recognition process. The findings include: the detection rate of human faces reaches 98.67%, the feature capture rate reaches 91.33%, and the average recognition rate of group classification reaches 90.60%.

書名頁 i
論文口試委員審定書 ii
授權書 iii
中文摘要 iv
英文摘要 v
誌謝 vii
目錄 ix
表目錄 xii
圖目錄 xiii
第一章　　緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 2
1.4 研究限制 3
1.5 論文章節組織 3
第二章　　文獻探討 4
2.1 相關研究 4
2.1.1 PCA轉換 4
2.2 影像處理技術 6
2.2.1 YCbCr色彩空間轉換 6
2.2.2 灰階轉換 7
2.2.3 低通濾波 7
2.2.4 Sobel邊緣偵測 8
2.2.5直方圖等化 8
2.2.6 影像二值化 8
2.2.7水平、垂直投影 9
2.2.8 圓形霍氏轉換 9
第三章　　研究架構 11
3.1 人臉區域擷取 13
3.2 特徵區域擷取 16
3.3 特徵區域最佳化 17
3.3.1 眼部區域最佳化 17
3.3.2 嘴部區域最佳化 19
3.4 特徵點定位 21
3.4.1 瞳孔特徵點 21
3.4.2 內眼角特徵點 23
3.4.3 嘴角特徵點 23
3.4.4 鼻部區域擷取 24
3.4.5 鼻頭特徵點 25
3.5 特徵值計算 26
3.5.1 線性特徵 27
3.5.2 非線性特徵 28
3.6 辨識方法 28
3.6.1 SimNet 28
3.6.2 SimNet訓練階段 31
3.6.3 SimNet辨識階段 31
第四章　　研究結果 32
4.1 自製人臉資料庫 33
4.2 實驗環境 33
4.3 系統畫面 33
4.3.1 特徵值擷取系統 34
4.3.2 分類系統 35
4.4 實驗結果 36
4.4.1 人臉偵測結果 36
4.4.2 特徵點定位結果 36
4.4.3 辨識結果 38
4.5 結果分析 38
第五章　　結論與未來展望 39
5.1 結論 40
5.2 未來展望 40
5.2.1提高表情變異及環境變異的辨識能力 40
5.2.2 創造高安全性系統 41
參考文獻 41

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