【摘要】：隨著科學(xué)技術(shù)的發(fā)展,安全問題越來越受到人們的關(guān)注。如何準(zhǔn)確可靠地識(shí)別出一個(gè)人的身份,已經(jīng)成為一個(gè)亟待解決的問題。傳統(tǒng)的身份識(shí)別機(jī)制存在容易盜取和復(fù)制的缺點(diǎn),已經(jīng)不能滿足人們對于高安全性的要求。生物特征識(shí)別技術(shù)是解決該問題的有效途徑,其中,掌靜脈識(shí)別技術(shù)是近年來興起的一種生物特征識(shí)別技術(shù),具有高防偽性、識(shí)別精度高和容易被用戶接受等優(yōu)點(diǎn)。本論文主要針對掌靜脈識(shí)別算法進(jìn)行了比較深入的研究,研究比較了ROI(Region of Interest,感興趣區(qū)域)圖像的獲取和ROI圖像的圖像增強(qiáng)和去噪方式,重點(diǎn)研究了NBP(Neighbor based Binary Pattern,近鄰二值模式)特征的提取匹配方法,SIFT(Scale Invariant Feature Transform,尺度不變特征)特征的提取匹配方法并提出了一種融合紋理特征和局部不變特征的掌靜脈識(shí)別方法。本論文的主要工作和研究成果如下:1.就掌靜脈識(shí)別ROI圖像的獲取和預(yù)處理進(jìn)行了初步研究和比對實(shí)驗(yàn),選取基于掌心矩形的ROI圖像提取方式和CLAHE(Contrast Limited Adaptive histogram equalization,限制對比度自適應(yīng)直方圖均衡)方法與中值濾波的方法對圖像進(jìn)行圖像增強(qiáng)和去噪,以達(dá)到后續(xù)試驗(yàn)的最好效果。2.研究了基于NBP特征和基于SIFT特征的兩種單特征的掌靜脈識(shí)別方法。針對SIFT匹配過程中傳統(tǒng)RANSAC(Random Sample Consensus,隨機(jī)抽樣一致算法)方法的弊端,提出了一種基于相似度距離剔除錯(cuò)誤匹配點(diǎn)的方法,提高錯(cuò)誤匹配點(diǎn)剔除的效率及準(zhǔn)確率,使SIFT算法在掌靜脈識(shí)別中的應(yīng)用更為準(zhǔn)確。然后,設(shè)計(jì)實(shí)驗(yàn)對兩種掌靜脈識(shí)別方式進(jìn)行比較分析,得到兩種算法性能特點(diǎn),為后續(xù)融合兩種算法提供依據(jù)和思路。3.針對前文研究的兩種算法的優(yōu)缺點(diǎn),通過對兩種算法融合的可行性進(jìn)行分析:NBP特征作為一種全局特征,SIFT作為一種局部特征,兩者對于不同手掌圖像的區(qū)分度和相同手掌圖像的匹配度具有較強(qiáng)的互補(bǔ)性,并且在實(shí)驗(yàn)時(shí)間NBP特征也可以彌補(bǔ)SIFT特征的不具有實(shí)時(shí)性的劣勢,在魯棒性上SIFT特征可以彌補(bǔ)NBP特征在較大位移上魯棒性差的劣勢,得到兩種算法非常適合進(jìn)行信息融合的結(jié)論,提出一種融合紋理特征和局部不變特征的掌靜脈識(shí)別算法,提高了掌靜脈算法的識(shí)別正確率,在PolyU掌紋庫和實(shí)驗(yàn)室自采庫上分別取得正確識(shí)別率99.114%和99.722%的效果。
[Abstract]:With the development of science and technology, people pay more and more attention to the security problem. How to identify a person accurately and reliably has become an urgent problem. The traditional identification mechanism is easy to steal and copy, which can not meet the requirements of high security. Biometric recognition technology is an effective way to solve this problem. Among them, metacarpal vein recognition technology is a kind of biometric recognition technology developed in recent years, which has the advantages of high anti-counterfeiting, high recognition accuracy and easy to be accepted by users. This paper mainly focuses on the palmar vein recognition algorithm, studies and compares the ROI (Region of Interest, image acquisition and ROI image enhancement and denoising methods. In this paper, the extraction and matching method of NBP (Neighbor based Binary Pattern, nearest neighbor binary pattern) feature is studied, and a method of palmar vein recognition based on texture feature and local invariant feature is proposed, which is based on sift (Scale Invariant Feature Transform, scale invariant feature. The main work and research results of this thesis are as follows: 1. The acquisition and preprocessing of ROI images of metacarpal vein recognition were preliminarily studied and compared. The method of ROI image extraction based on palm rectangle and CLAHE (Contrast Limited Adaptive histogram equalization, restricted contrast adaptive histogram equalization method and median filter method are selected to enhance and de-noise the image so as to achieve the best effect of subsequent experiments. 2. Two methods of palmar vein recognition based on NBP feature and SIFT feature are studied. In view of the disadvantages of the traditional RANSAC (Random Sample Consensus, random sampling algorithm in SIFT matching process, a method based on similarity distance is proposed to eliminate the error matching points, which can improve the efficiency and accuracy of error matching points elimination. The application of SIFT algorithm in metacarpal vein recognition is more accurate. Then, the experiment is designed to compare and analyze the two methods of palmar vein recognition, and the performance characteristics of the two algorithms are obtained, which provide the basis and train of thought for the subsequent fusion of the two algorithms. In view of the advantages and disadvantages of the two algorithms mentioned above, this paper analyzes the feasibility of the fusion of the two algorithms by analyzing the SIFT as a global feature, and sift as a local feature by analyzing the feasibility of the fusion of the two algorithms. The two have strong complementarities for different palm image differentiation and the same palm image matching degree, and the NBP feature can also make up for the disadvantage of non-real-time SIFT feature in the experimental time. In terms of robustness, SIFT features can make up for the disadvantage of poor robustness of NBP features on large displacement. It is concluded that the two algorithms are very suitable for information fusion. A palmar vein recognition algorithm based on texture feature and local invariant feature is proposed. The recognition rate of metacarpal vein algorithm was improved, and the correct recognition rates were 99.114% and 99.722% in PolyU palmprint database and laboratory self-mining database, respectively.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 林森;吳微;苑瑋琦;;采用紋理近鄰模式的掌靜脈生物特征識(shí)別研究[J];儀器儀表學(xué)報(bào);2015年10期

2 桑海峰;武紅嬌;何大闊;;手形、掌紋和掌靜脈多特征融合識(shí)別[J];儀器儀表學(xué)報(bào);2015年06期

3 吳微;苑瑋琦;林森;宋輝;桑海峰;;基于灰度曲面匹配的快速手掌靜脈識(shí)別[J];光學(xué)學(xué)報(bào);2013年10期

4 吳微;苑瑋琦;林森;宋輝;張洪濤;;手掌靜脈識(shí)別中感興趣區(qū)域的選擇與定位研究[J];光電子.激光;2013年01期

5 桑海峰;趙云;苑瑋琦;陳靜;;基于人手自然張開的多生物特征識(shí)別[J];儀器儀表學(xué)報(bào);2011年11期

6 李秀艷;劉鐵根;鄧仕超;何瑾;王云新;;基于SURF算子的快速手背靜脈識(shí)別[J];儀器儀表學(xué)報(bào);2011年04期

7 孫浩;王程;王潤生;;局部不變特征綜述[J];中國圖象圖形學(xué)報(bào);2011年02期

8 秦斌;;手靜脈身份識(shí)別技術(shù)[J];現(xiàn)代電子技術(shù);2011年04期

9 苑瑋琦;董茜;桑海峰;;基于方向梯度極值的手形輪廓跟蹤算法[J];光學(xué)精密工程;2010年07期

10 王云新;劉鐵根;江俊峰;張忠傳;周蘇晉;;基于局部SIFT分析的手背靜脈識(shí)別[J];光電子.激光;2009年05期

相關(guān)博士學(xué)位論文 前6條

1 顏學(xué)葵;掌靜脈識(shí)別算法研究[D];華南理工大學(xué);2015年

2 李威;非接觸成像方式下手掌特征提取方法研究[D];沈陽工業(yè)大學(xué);2013年

3 張環(huán);掌紋掌脈及其融合識(shí)別技術(shù)研究[D];國防科學(xué)技術(shù)大學(xué);2011年

4 李強(qiáng);掌靜脈身份識(shí)別技術(shù)的理論與實(shí)驗(yàn)研究[D];華中科技大學(xué);2010年

5 李鐵鋼;靜脈識(shí)別算法研究[D];吉林大學(xué);2007年

6 祝恩;低質(zhì)量指紋圖像的特征提取與識(shí)別技術(shù)的研究[D];國防科學(xué)技術(shù)大學(xué);2005年

相關(guān)碩士學(xué)位論文 前8條

1 梅尚健;基于特征融合的圖像檢索研究與實(shí)現(xiàn)[D];西南交通大學(xué);2015年

2 孟昭慧;基于二次判別和局部信息及特征融合的手靜脈識(shí)別[D];復(fù)旦大學(xué);2014年

3 胡云朋;基于多特征融合的手背靜脈識(shí)別算法研究[D];天津理工大學(xué);2014年

4 方婷;全手掌靜脈識(shí)別算法研究[D];沈陽工業(yè)大學(xué);2013年

5 郭晶;基于特征點(diǎn)的多幅圖像拼接技術(shù)研究[D];西安科技大學(xué);2012年

6 佟海濱;手掌靜脈識(shí)別系統(tǒng)[D];沈陽工業(yè)大學(xué);2012年

7 陳梓毅;基于掌紋和手背靜脈的多模態(tài)生物認(rèn)證系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)[D];華南理工大學(xué);2011年

8 陳志雄;基于圖像配準(zhǔn)的SIFT算法研究與實(shí)現(xiàn)[D];武漢理工大學(xué);2008年

，

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