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Author: 張富貿
Fu-Mao Chang
Thesis Title: 基於深度學習方法的非接觸式掌靜脈辨識系統
A Contactless Palm Vein Recognition System Based on Deep Learning Method
Advisor: 洪西進
Shi-Jinn Horng
Committee: 謝仁偉
Jen-Wei Hsieh
顏成安
Cheng-An Yen
Degree: 碩士
Master
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2020
Graduation Academic Year: 108
Language: 中文
Pages: 47
Keywords (in Chinese): 生物辨識掌靜脈辨識非接觸式系統UVC 鏡頭
Keywords (in other languages): Biometrics, Palm Vein Identification, Contactless System, UVC Camera
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近年來科技的迅速發展,使得人們的生活越來越便利,相對於言,資訊安全議題則越來越受到大家的重視,身分識別系統逐漸被應用在各種相關環境中。生物特徵辨識是近年來火熱的話題,利用生物特徵的唯一性來做身分識別系統,已成為資訊領域研究方向之一。
由於掌靜脈辨識具有活體資訊之特性,且人體靜脈資訊於成年後其結構已穩定分布,因此非常適合應用在生物特徵辨識系統上。然而,之前的掌靜脈辨識系統的擷取影像方式是擺放在固定機構上面,容易受到使用者擺放位置而影響辨識率。本論文是採取非接觸式的方式擷取掌靜脈影像,其困難度遠比固定式機構還要大,因此如何有效地減少使用者造成的手掌位移、遠近、旋轉而造成之影響將會是決定此辨識系統效能之關鍵。
本論文將辨識系統劃分為兩階段運作,透過第一階段二值化影像之方式,去計算感興趣區域的位置,接著將感興趣區域的影像放入深度學習的模組訓練,完成訓練後,再用準備好的測試集測試,取得最終辨識結果。


The rapid development of science and technology in recent years has made people live more and more convenient. Relatively, people pay much attention on information security and identity recognition systems are gradually applied in various related environments. Biometrics is a hot research topic in recent years. Therefore, using the uniqueness of biometrics as an identity recognition system has gradually become one of the research directions in the field of computer science.
Since palm vein has the characteristics of living body information and the structure of human vein information is stable after grown up, palm vein is suitable for biometric identification system. However, the image capturing method of the previous palm vein recognition system is placed on a fixed mechanism, which is very easily influenced by the displacement of user's palm position. In this thesis, we use a contactless method to capture the palm vein images. It is more difficult than the fixed mechanism. How to effectively reduce the impact of the palm displacement, distance changing, and rotation caused by the user will be the key factors to determine the performance of this recognition system.
In this thesis, the identification system is divided into two stages. In the first stage, the captured palm vein image is binaried and the region of interest is obtained. Then in the second stage, the image of the region of interest is put into the deep learning network for training. Finally, a prepared test dataset is used to do testing and the best recognition results are obtained.

摘要 I ABSTRACT II 致謝 III 目錄 IV 圖目錄 VI 第一章 緒論 1 1.1 研究動機與目的 1 1.2 相關研究回顧 1 1.3 論文章節介紹 2 第二章 系統架構 4 2.1 系統機構 4 2.2 系統流程 5 (一) 系統註冊機制(System Registration) 5 (二) 系統辨識流程(System Recognition) 5 第三章 掌靜脈抓取ROI前處理 7 3.1 二值化前處理(PRE-BINARIZATION) 8 3.2 CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION (CLAHE) 9 3.3 全域二值化(SIMPLE TRESHOLDING) 11 3.4 開運算 12 3.5 抓取輪廓 14 3.6 找尋手指間谷點 19 3.7 用特徵點框ROI 25 3.8 小結(SUMMARY) 26 第四章 深度學習介紹 27 4.1 殘差深度網路(RESIDUAL NEURAL NETWORK) 27 4.2 殘差網路的改進 27 4.3 殘差網路架構 29 4.4 梯度最佳解演算法(OPTIMIZED ALGORITHM OF GRADIENT) 31 4.5 損失函數(LOSS FUNCTION) 35 4.6 小結 36 第五章 實驗結果 37 5.1 系統開發環境 37 5.2 系統執行畫面 37 5.3 掌靜脈資料庫 40 第六章 結論 42 第七章 未來展望 43 參考文獻 44

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