博碩士論文 102522007 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:6 、訪客IP:3.229.118.253
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)
相關論文
★ 基於QT之跨平台無線心率分析系統實現★ 網路電話之額外訊息傳輸機制
★ 基於虹膜色彩空間的極端學習機的多類型頭痛分類★ 以多分數加權融合方式進行虹膜影像品質檢定
★ 基於深度學習之工業用智慧型機器視覺系統:以文字定位與辨識為例★ 基於深度學習的即時血壓估測演算法
★ 基於深度學習之工業用智慧型機器視覺系統:以焊點品質檢測為例★ 針對與運動比賽精彩畫面相關串場效果之偵測
★ 植基於向量量化之視訊/影像內容驗證技術★ 植基於串場效果偵測與內容分析之棒球比賽精華擷取系統
★ 以視覺特徵擷取為基礎之影像視訊內容認證技術★ 使用動態背景補償以偵測與追蹤移動監控畫面之前景物
★ 應用於H.264/AVC視訊內容認證之適應式數位浮水印★ 棒球比賽精華片段擷取分類系統
★ 利用H.264/AVC特徵之多攝影機即時追蹤系統★ 利用隱式型態模式之高速公路前車偵測機制
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]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

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
參考文獻 [1] Y. Lecun, L. Bottom, Y. Bengio, and P. Haffner, “Gradient-based learning
applied to document recognition,” in Proc. of the IEEE, Nov. 1998, vol. 86,
pp. 2278–2324.
[2] vdumoulin, “Convolution arithmetic,” https://github.com/vdumoulin/
conv_arithmetic, 2016, [Online] Available.
[3] J. Daugman, “How iris recognition works,” IEEE Transactions on Circuits
and Systems for Video Technology, vol. 14, no. 1, pp. 21–30, Jan. 2004.
[4] J. Daugman, “High confidence visual recognition of persons by a test of statistical
independence,” IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 15, no. 11, pp. 1148–1161, Nov. 1993.
[5] J. Daugman, “New method in iris recgonition,” IEEE Transactions on Systems,
Man, and Cybernetics, Part B, vol. 37, no. 5, pp. 1167–1175, Oct. 2007.
[6] R. P. Wildes, “Iris recognition: an emerging biometric technology,” in Proc.
of the IEEE, Sep. 1997, vol. 85, pp. 1348–1363.
[7] L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal identification based on
iris texture analysis,” IEEE Transactions on Pattern analysis and Machine
Intelligence, vol. 25, no. 12, pp. 1519–1533, Dec. 2003.
[8] C. Tisse, L. Martin, L. Torres, and M. Robert, “Person identification technique
using human iris recognition,” in Proc. Vision Interface, 2002, pp.
294–299.
[9] D. Zhang, D. M. Monro, and S. Rakchit, “eyelash removal method for human
iris recognition,” in IEEE International Confernece on Image Processing,
Oct. 2006, pp. 285–288.
[10] Y. Li and M. savvides, “An automatic iris occlusion estimation method
based on high-dimensional density estimation,” IEEE Transactions on Pattern
analysis and Machine Intelligence, vol. 35, no. 4, pp. 784–796, APR.
2013.
[11] M. Haindl and M. Krupicka, “Accurate detection of non-iris occlusions,”
in 2014 Tenth International Conference on Signal-Image Technology and
Internet-Based Systems, Nov 2014, pp. 49–56.
[12] A. Gangwar, A. Joshi, A. Singh, F. Alonso-Fernandez, and J. Bigun, “Irisseg:
A fast and robust iris segmentation framework for non-ideal iris images,” in
2016 International Conference on Biometrics (ICB), June 2016, pp. 1–8.
[13] Maroti Deshmukh and Munaga V. N. K. Prasad, Partial Segmentation and
Matching Technique for Iris Recognition, pp. 77–86, Springer India, 2015.
[14] N. Liu, H. Li, M. Zhang, Jing Liu, Z. Sun, and T. Tan, “Accurate iris segmentation
in non-cooperative environments using fully convolutional networks,”
in 2016 International Conference on Biometrics (ICB), June 2016, pp. 1–8.
[15] K. Bae, S. Noh, and J. Kim, “Iris feature extraction using independent component
analysis,” in Proc. of the 2003 4th International Conference on Audio
and Video-Based Biometric Person Authentication, 2003, pp. 838–844.
[16] E. Krichen, S. Garcia-Salicetti, and B. Dorizzi, “A new probabilistic iris
quality measure for comprehensive noise detection,” in IEEE International
Confernece on Biometrics: Theory, Applications, and Systems, Sept. 2007,
pp. 1–6.
[17] J. Thornton, Matching deformed and occluded iris patterns: a probabilistic
model based on discriminative cues, PhD thesis, Carnegie Mellon University,
2007.
[18] S. Attarchi, K. Faez, and A. Asghari, “A fast and accurate iris recognition
method using the complex inversion map and 2dpca,” in Proc. of the IEEE
International Conference on 2008 Computer and Information Science, Apr.
2008, pp. 179–184.
[19] G. E. Hinton and R. Salakhutdinov, “Reducing the dimensionality of data
with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, Jul. 2006.
[20] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for
semantic segmentation,” in Conputer Vision and Pattern Recognition, 2015.
[21] L. Xu, C. Lu, Y. Xu, and J. Jia, “Image smoothing via l0 gradient minimization,”
ACM transactions on Graphics, vol. 30, no. 5, pp. 174:1–174:12, Dec.
2011.
指導教授 蘇柏齊、栗永徽(Po-Chyi Su Yung-Hui Li) 審核日期 2017-1-23
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明