本文選題：掌靜脈識(shí)別　切入點(diǎn)：OTSU　出處：《北京交通大學(xué)》2017年碩士論文

【摘要】：掌靜脈識(shí)別技術(shù)是通過(guò)分析手掌里的靜脈血管信息進(jìn)行身份識(shí)別的一項(xiàng)生物特征識(shí)別技術(shù),具有識(shí)別準(zhǔn)確、活體認(rèn)證、易于接受等優(yōu)點(diǎn),近年來(lái)得到了廣泛的關(guān)注,并成功地應(yīng)用于安檢、金融、社保資金發(fā)放等領(lǐng)域。目前對(duì)于掌靜脈識(shí)別技術(shù)的研究大多集中于對(duì)特征提取算法的研究,對(duì)于掌靜脈圖像獲取之后的預(yù)處理算法研究的并不多,然而對(duì)于非接觸式采集掌靜脈圖像,每次采集時(shí)手掌的位置、方向以及手指的張開(kāi)角度都會(huì)有所不同,因此預(yù)處理的結(jié)果將直接影響到最后識(shí)別的準(zhǔn)確性。為解決上述這些問(wèn)題,本文首先對(duì)掌靜脈圖像的預(yù)處理進(jìn)行了比較詳細(xì)的研究和討論,然后提出了一種新的掌靜脈特征提取算法和匹配算法,取得了比較滿(mǎn)意的識(shí)別效果。論文主要研究工作如下:(1)針對(duì)傳統(tǒng)的掌靜脈識(shí)別算法沒(méi)有考慮獲取感興趣區(qū)域(Region Of Interest,ROI)的各種復(fù)雜情況,而非接觸式采集到的掌靜脈圖像引入的噪聲等各種不可控因素較多,本文對(duì)預(yù)處理算法進(jìn)行了系統(tǒng)的定量研究——對(duì)基于固定長(zhǎng)度和相對(duì)長(zhǎng)度的ROI獲取進(jìn)行了對(duì)比實(shí)驗(yàn)。(2)提出了一種新的基于主曲率的掌靜脈特征提取算法。首先對(duì)ROI進(jìn)行梯度標(biāo)準(zhǔn)化,去除干擾噪聲;然后求取圖像的最大主曲率,將掌靜脈結(jié)構(gòu)同周?chē)娜梭w組織區(qū)分出來(lái);最后再對(duì)圖像進(jìn)行分割腐蝕提取出了清晰的掌靜脈特征。(3)提出了基于模版匹配的掌靜脈特征匹配算法。在掌靜脈圖像的特征匹配階段,針對(duì)主曲率提取出的掌靜脈特征,使用模版匹配法進(jìn)行識(shí)別,并且和LBP匹配法進(jìn)行對(duì)比。實(shí)驗(yàn)證明ROI的邊長(zhǎng)取1.5倍左右的食指、中指間的谷點(diǎn)和無(wú)名指、拇指間的谷點(diǎn)之間的距離時(shí),可以達(dá)到最好的識(shí)別效果,并且主曲率法結(jié)合模板匹配法可以達(dá)到1.965%的等誤率(Equal Error Rate,EER),識(shí)別效果準(zhǔn)確。
[Abstract]:Metacarpal vein recognition is a biometric identification technology based on the analysis of venous vascular information in the palm. It has the advantages of accurate identification, living authentication and easy to accept, and has been paid more and more attention in recent years. And it has been successfully applied in the fields of security, finance, social security fund distribution and so on. At present, most of the researches on palmar vein recognition are focused on the feature extraction algorithm, and there are few researches on the pre-processing algorithm after the palmar vein image acquisition. However, for non-contact acquisition of metacarpal vein images, the position of the palm, the direction of the palm and the angle of the opening of the fingers are different with each acquisition. Therefore, the result of preprocessing will directly affect the accuracy of final recognition. In order to solve these problems, this paper firstly studies and discusses the preprocessing of metacarpal vein image in detail. Then a new palmar vein feature extraction algorithm and matching algorithm are proposed. The main work of this paper is as follows: 1) for the traditional palmar vein recognition algorithm, the region of interest (region of interest) is not taken into account. However, there are many uncontrollable factors such as noise and other uncontrollable factors in the non-contact image of palmar vein. In this paper, a systematic quantitative study of preprocessing algorithm is carried out. A new algorithm for extraction of metacarpal vein features based on main curvature is proposed. Firstly, the ROI is extracted by comparing the ROI acquisition based on fixed length and relative length. For gradient standardization, The interference noise is removed, the maximum principal curvature of the image is obtained, and the palmar vein structure is distinguished from the surrounding human tissue. Finally, a palmar vein feature matching algorithm based on template matching is proposed. In the phase of metacarpal vein image feature matching, the palmar vein feature is extracted from the principal curvature. The template matching method is used to identify and compare with the LBP matching method. The experiment proves that the best recognition effect can be achieved when the side length of ROI is about 1.5 times that of index finger, the distance between middle fingers and ring fingers, and the distance between thumbs and valley points. The principal curvature method combined with template matching method can achieve equal Error error rate of 1.965%, and the recognition effect is accurate.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 吳微;苑瑋琦;;手掌靜脈圖像識(shí)別技術(shù)綜述[J];中國(guó)圖象圖形學(xué)報(bào);2013年10期

2 吳微;苑瑋琦;林森;孔德奇;張洪濤;;手掌靜脈識(shí)別典型波長(zhǎng)選擇[J];光學(xué)學(xué)報(bào);2012年12期

相關(guān)博士學(xué)位論文 前1條

1 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

相關(guān)碩士學(xué)位論文 前2條

1 王玲;基于LBP的特征提取研究[D];北京交通大學(xué);2009年

2 汪銳;手掌靜脈識(shí)別研究[D];哈爾濱工業(yè)大學(xué);2008年

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本文編號(hào)：1693663