博碩士論文 102522007 詳細資訊




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姓名 李孟桓(Meng-Huan Li)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 應用卷積神經網路的虹膜遮罩預估
(Robust and Accurate Iris Mask Estimation using Convolutional Neural Network)
檔案 [Endnote RIS 格式]    [Bibtex 格式]    至系統瀏覽論文 (2019-3-1以後開放)
摘要(中) 生物特徵辨識是指基於一個人的生理或者行為特徵作為身分辨識機處的一種技術,虹膜辨識是生物特徵辨識中一種精確度、普遍性、獨特性很高,且侵入性很低的辨識方式。在一個典型的虹膜辨識系統當中包含了以下幾個階段:1. 影像擷取、2. 虹膜切割、3. 虹膜遮罩產生、4. 特徵提取、5. 特徵比對,為了提高虹膜辨識的準確率,許多的研究裡都關注在如何正確切割虹膜、提取特徵以及特徵比對,然而虹膜遮罩的正確與否也是虹膜辨識準確性的重要因素之一。
在本篇論文中,我們嘗試了多種的神經網路架構來對虹膜遮罩進行預估,最後提出了兩種基於深度學習(Deep Learning) 的演算法來學習輸入虹膜影像的遮罩,我會將虹膜影像和其對應正確的虹膜遮罩做些許前處理後,輸入進我們建置好的深度學習網路學習其特徵,學習完特徵後的網路在輸入新的虹膜影像時也能順利的預測其對應虹膜影像遮罩,使產生虹膜遮罩的正確率相對於rule-based 或其他演算法產生的虹膜遮罩高,且能提升虹膜辨識最終的準確性,使用patch-based CNN 的虹膜遮罩正確率可以達到92.87%、EER 為0.147%,使用multi-channel FCN 的虹膜遮罩正確率可以達到95.56%、EER 為0.0851%。
摘要(英) Iris recognition has a lot of applications. A typical iris recognition system has several stages, including acquisition, segmentation, iris mask generation, feature extraction and matching. In order to increase the accuracy of iris recognition, many studies focus on iris segmentation, feature extraction and matching. However, iris masks can also have a great impact on the accuracy of recognition.
In this study, we propose two iris mask estimation algorithm based on deep learning. After pre-processing the iris images and the corresponding masks, we train these data in convolution neural networks (CNN), which help to achieve a higher accuracy in matching iris masks for different images than rule-based algorithms. The accuracy of matching by using patch-based CNN is 92.87%, with the 0.147% EER (Equal Error Rate) and the accuracy of applying multi-channel fully convolution networks is 95.56%, with an even lower EER equal to 0.0851%.
關鍵字(中) ★ 深度學習
★ 卷積網路
★ 全卷積網路
★ 虹膜辨識
★ 虹膜遮罩
關鍵字(英) ★ Deep learning
★ CNN
★ FCN
★ Iris recognition
★ Iris mask
★ Iris occlusion
論文目次 中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
英文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
圖目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
表目錄. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
一、緒論. . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1-1 研究動機. . . . . . . . . . . . . . . . . . . . . . . . . 1
1-2 系統流程. . . . . . . . . . . . . . . . . . . . . . . . . 2
1-3 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . 3
二、相關研究與文獻回顧. . . . . . . . . . . . . . . . . . . 6
2-1 虹膜辨識基礎介紹. . . . . . . . . . . . . . . . . . . . 6
2-2 虹膜辨識系統. . . . . . . . . . . . . . . . . . . . . . 7
2-3 虹膜遮罩相關. . . . . . . . . . . . . . . . . . . . . . 8
2-4 其他虹膜辨識相關文獻. . . . . . . . . . . . . . . . . 11
三、深度學習網路介紹. . . . . . . . . . . . . . . . . . . . 12
3-1 類神經網路. . . . . . . . . . . . . . . . . . . . . . . . 13
3-2 類神經網路原理. . . . . . . . . . . . . . . . . . . . . 14
3-3 多層類神經網路. . . . . . . . . . . . . . . . . . . . . 16
四、應用深度學習網路的虹膜遮罩預估. . . . . . . . . . . 22
4-1 卷積神經網路. . . . . . . . . . . . . . . . . . . . . . 23
4-2 全卷積網路. . . . . . . . . . . . . . . . . . . . . . . . 25
4-2-1 全卷積網路改善. . . . . . . . . . . . . . . . . . . . . 28
4-3 影像的前處理. . . . . . . . . . . . . . . . . . . . . . 28
4-3-1 Image Smoothing via L0 Gradient Minimization . . . . . 29
4-3-2 一維模糊化. . . . . . . . . . . . . . . . . . . . . . . . 29
4-3-3 二維模糊化. . . . . . . . . . . . . . . . . . . . . . . . 30
五、實驗. . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
5-1 使用的訓練及測試資料. . . . . . . . . . . . . . . . . 35
5-1-1 Rule-based 的虹膜影像遮罩產生. . . . . . . . . . . . 36
5-2 使用卷積神經網路虹膜遮罩預估. . . . . . . . . . . . 37
5-3 使用全卷積神經網路架構的虹膜遮罩預估. . . . . . . 40
六、結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
索引. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
參考文獻. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
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指導教授 蘇柏齊 栗永徽(Po-Chyi Su Yung-Hui Li) 審核日期 2017-1-23
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