博碩士論文 106553004 詳細資訊




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姓名 王曼菱(Man-Ling Wang)  查詢紙本館藏   畢業系所 通訊工程學系在職專班
論文名稱 掌紋紋理特徵擷取與身分識別
(Texture Feature Extraction and Classification for Palmprint Identity Verification)
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2025-6-30以後開放)
摘要(中) 本研究提出一個多模態掌紋身分識別方法,本系統透過智慧型手機拍攝手掌影像,採用基於最大內切圓的方法擷取感興趣的掌紋區域,再使用灰度共生矩陣( GLCM)、灰度梯度共生矩陣(GGCM)、局部二值模式(LBP),三種紋理特徵提取方式提取掌紋紋理特徵向量,分別將三組特徵向量結合機率神經網路分類器(PNN),以得到三組最佳的推論機率,最後我們將每一個模組的推論機率進行加權融合,得到辨識結果。針對自建的手掌影像資料庫實驗結果顯示,在訓練資料為10筆的情況下,GLCM、GGCM、LBP三種模態各自的準確率分別為82%、86%及89%,結合決策融合PNN的準確率則提升為96%。我們還使用穆塔大學(Mutah University)提供的公開掌紋資料庫所進行比較實驗,準確率可達99.5%,相較於VGG-19和AlexNet,我們的多模態掌紋身分識別系統具有良好的辨識性能。
摘要(英) This study proposes a multimodal palm tattoo recognition method. This system captures palm images through a smartphone, using the method based on the largest inscribed circle to capture the area of interest palm print, and then uses the gray level co-occurrence matrix (GLCM), gray scale Gradient co-occurrence matrix (GGCM) and local binary pattern (LBP), three texture feature extraction methods to extract palmprint texture feature vectors respectively, and three sets of feature vectors are combined with a probabilistic neural network classifier (PNN) to obtain the three sets of best inferences probability. Finally, we weighted and fused the inference probabilities of each module to obtain the recognition result. The experimental results of the self-built palm image database show that with 10 training data, the accuracy of the three modalities of GLCM, GGCM, and LBP are 82%, 86%, and 89%, respectively. The accuracy was increased to 96%. We also use the public palm print database provided by Mutah University for comparative experiments, and the accuracy can reach 99.5%. Compared with VGG-19 and AlexNet, our multi-modal palm tattoo sub-recognition system has good recognition performance.
關鍵字(中) ★ 掌紋
★ 特徵擷取
★ 身分識別
關鍵字(英)
論文目次 摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VII
表目錄 X
第1章、 緒論 1
1.1 研究背景 1
1.2 研究目標 3
1.3 論文架構 3
第2章、 方法回顧 4
2.1 感興趣區域(ROI)提取 4
2.1.1 基於切線的方法 5
2.1.2 基於輪廓距離分佈的方法 6
2.1.3 基於最大內切圓的方法 7
2.2 掌紋特徵提取 8
2.2.1 灰度共生矩陣 8
2.2.2 灰度梯度共生矩陣 13
2.2.3 局部二值模式 17
2.3 機率神經網路 19
2.3.1 貝氏分類器 20
2.3.2 Parzen視窗法 21
2.3.3 PNN分類器架構 22
第3章、 掌紋身分識別系統設計 24
3.1 系統架構 24
3.2 影像預處理 25
3.3 紋理特徵提取模組設計 29
3.3.1 GLCM模組設計 29
3.3.2 GGCM模組設計 32
3.3.3 LBP模組設計 34
3.4 掌紋分類器設計 37
3.4.1 紋理PNN分類器 38
3.4.2 決策融合分類器 40
第4章、 實驗 41
4.1 實驗環境 41
4.2 實驗資料庫建置 42
4.3 識別性能實驗 46
4.4 決策融合實驗 52
第5章、 結論與未來展望 59
5.1 結論 59
5.2 未來展望 60
參考文獻 61
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指導教授 林銀議 陳慶瀚(Yin-Yi Lin Ching-Han Chen) 審核日期 2020-6-30
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