生物特徵識別（Biometric identification）是目前熱門研究的主題之一，其中有一部份運用手部的特徵像是指紋（Fingerprint）、手的幾何（Hand geometry）、掌紋（Palmprint）與掌背血管（Vein of Palm-dorsum）等。其中，掌紋的特徵由主線（Principal lines）、皺褶（Wrinkes）與脊（ridges）所組成，而人類的整根手指上的紋路與手掌上的紋路亦有近似的三項特徵結構，即主線、皺褶、脊。因此，本論文嘗試取出手掌影像中的小指、無名指、中指和食指共四根手指上中央部位的長方形手指紋路（Finger Crease Patterns）當作興趣區域（Regions of Interest, ROI），再以小波轉換（Wavelet transformation）來取得小波能量特徵（Wavelet Energy Features, WEF），最後藉由倒傳遞類神經網路（Back propagation neural network, BPNN）來作比對。

Biometric identification is one of the popular research fields recently. Some of those use the features of hands like fingerprint, hand geometry, palmprint and vein of palm-dorsum. Palmprint has several features, which include principal lines, wrinkles and ridges. Also, the whole stick of finger is full of the similar structure of features (i.e. principal lines, wrinkles and ridges). In this dissertation, a new approach is introduced. In the image of the palm, we try to use Finger Crease Patterns on the central area of four fingers (little finger, ring finger, middle finger and forefinger) as regions of Interest (ROI). Next we computed the wavelet energy feature (WEF) through the use of wavelet transform. Finally, the back propagation neural network (BPNN) is applied for verification.

目錄
目錄 VII
圖表目錄 IX
1 第一章 緒論 1
1.1 研究動機 1
1.2 相關文獻 3
1.2.1手的幾何 3
1.2.2掌紋 5
1.2.3手指紋路 6
1.2.4多重模式 7
2 第二章 小波理論及倒傳遞類神經網路 9
2.1 小波理論 9
2.1.1 一維Haar小波轉換 9
2.1.2 二維Haar小波轉換 12
2.1.3 以濾波器的觀點來看小波轉換 13
2.2 倒傳遞類神經網路[20] 14
2.2.1倒傳遞演算法[20] 15
3 第三章 研究方法 18
3.1 前處理階段 18
3.1.1 影像二值化 19
3.1.2 侵蝕與擴張 21
3.1.3 邊緣描繪 23
3.1.4 手指端點定位 26
3.1.4.1 利用視窗結構定位手指端點 26
3.1.4.2 利用手掌輪廓與定點的距離分佈定位手指端點 28
3.1.5 擷取手指影像 34
3.1.6 手指節定位 36
3.1.7擷取手指內部的ROI影像 42
3.2 WEF特徵擷取階段 43
4 第四章 實驗設計與結果 45
4.1 實驗設計 45
4.2 WEF辨識結果 47
4.3 WEF的不同階數及頻帶辨識結果 48
4.4 不同的手指組合辨識結果 50
4.5 利用倒傳遞類神經網路辨識結果 51
4.6 其它相關文獻參考 52
5 第五章 結論與展望 53
6 參考文獻 54

6參考文獻
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