結合紋理特徵和幾何特徵的指紋影像品質評估

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姓名

安晨碩(CHEN-SHUO AN) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

結合紋理特徵和幾何特徵的指紋影像品質評估
(Fingerprint Image Quality Evaluation Combining Texture and Geometric features)

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摘要(中)

指紋影像評估主要應用於指紋感測器的影像品質評估。一個設計良好的指紋影像評估系統同時有助於提升指紋註冊的正確性，並有效提升指紋辨識率。本研究提出了一個結合紋理特徵分析及機率神經網路(Probability Neural Network，PNN)的指紋影像評估系統，以便改良現有的指紋影像品質評估方法。本研究的指紋影像評估流程可分為紋理特徵分析，PNN神經網路分類，整合PNN分類結果與傳統NFIQ評估，得到最終品質分數。就實驗結果顯示，結合紋理特徵分析的指紋影像評估可以提升評估指紋品質時的精確度。

摘要(英)

Fingerprint image assessment is primarily applied to assess the quality of images stored in fingerprint readers. A satisfactorily designed fingerprint image assessment system effectively improves the accuracy of fingerprint registration and the rate of fingerprint recognition. This study incorporated a new fingerprint image assessment system that combines fingerprint texture analysis with a probability neural network (PNN) to improve the existing fingerprint image quality assessment method. The fingerprint image assessment procedure involved texture analysis and PNN neural network classification. Subsequently, the PNN classification results were organized and compared to the conventional National Institute of Standards and Technology fingerprint image quality assessment results to obtain the final quality scores. According to the result of the experiment, the fingerprint image assessment system that incorporated texture analysis improved the accuracy of fingerprint image quality assessment.

關鍵字(中)

★ 指紋影像
★ 紋理特徵
★ 品質評估
★ 機率神經網路

關鍵字(英)

論文目次

摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章、緒論 1
1.1 研究背景 1
1.2 研究目的 3
1.3 論文架構 3
第二章、文獻回顧 4
2.1 簡介 4
2.2 NFIQ指紋影像品質的定義 4
2.3 計算指紋影像品質 4
2.3.1 特徵擷取 5
2.3.2 評估指紋特徵點品質: 8
2.3.3 特徵向量 8
2.3.4 訓練神經網路 10
2.4 評估指紋影像品質分數 10
2.5 紋理分析 10
2.6 PNN機率神經網路 12
第三章、指紋影像品質評估方法設計 17
3.1 MIAT方法論 17
3.2 IDEF0 18
3.3 Grafcet離散事件建模 19
3.4 指紋影像品質評估架構 22
3.5 指紋影像品質評估設計 25
3.5.1紋理特徵品質評估 26
3.5.2品質計算 27
第四章、實驗 28
4.1 實驗環境 28
4.2 指紋影像品質評估平台 32
4.2.1指紋影像品質評估平台實作 32
4.2.2 指紋影像品質評估程序 35
4.3指紋影像品質評估實驗 37
4.4品質與辨識性能的相關性分析 42
第五章、結論與未來方向 50
5.1 結論 50
5.2 未來方向 51
參考文獻 52

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指導教授

陳慶瀚

審核日期

2016-6-23

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