【摘要】：生物識別技術(shù)逐漸受到人們的重視和市場的迫切需求,已經(jīng)被不斷的研究與應(yīng)用。虹膜識別技術(shù)是該技術(shù)中的一種,在人體生物識別技術(shù)中倍受青睞。因其具有的獨特優(yōu)越的生理結(jié)構(gòu),比如:唯一性、穩(wěn)定性、防偽性,使之成為當(dāng)今社會個人身份鑒別的重要手段,而且在國防、安防、電子商務(wù)、金融等諸多領(lǐng)域中具有廣泛的應(yīng)用前景。本文對虹膜識別系統(tǒng)進行了深入研究與分析,提出了一種四向掃描法快速精確定位虹膜內(nèi)外邊緣;雙極性坐標(biāo)歸一化虹膜區(qū)域和該區(qū)域模塊劃分;改進的基于25個方向的二維Gabor濾波器特征提取;改進的基于Ferns分類器方法進行虹膜特征訓(xùn)練和樣本匹配測試。以上本文提出改進算法主要集中在對虹膜圖像進行預(yù)處理、特征提取和模式匹配三個部分的模塊研究中,具體的詳細(xì)敘述:第一,預(yù)處理:虹膜定位是虹膜圖像預(yù)處理模塊的關(guān)鍵環(huán)節(jié),本文采用的是基于四向掃描法的虹膜內(nèi)外邊緣快速精確定位方法。首先采用基于改進Canny算子和小波變換所提取的邊緣進行邊緣圖像融合獲取一個足夠封閉的內(nèi)邊緣,然后進行四向掃描分割出瞳孔,在進行重新確定邊緣點,計算內(nèi)邊緣圓心半徑,然后根據(jù)內(nèi)邊緣圓心半徑粗定位外邊緣圓心半徑,最后根據(jù)微分、積分運算獲取精確的外邊緣信息,從而精確的定位虹膜內(nèi)外邊緣;為獲取更純的虹膜紋理特征信息,在進行特征提取前,需要做的預(yù)處理包括圖像歸一化和圖像增強,本文在歸一化中采用的是雙極坐標(biāo)歸一化,然后進行模塊劃分,盡量選取更多的、更加豐富的、純的虹膜紋理信息,用均衡化增強處理。第二,特征提取:特征提取模塊采取的是基于5*5方向改進的二維Gabor濾波器的使用。從選取的虹膜區(qū)域提取圖像特征,再對Gabor濾波器的各項特性重新分析和參數(shù)設(shè)置,對25個紋理方向的特征提取融合,根據(jù)特征點進行計算和特征編碼,獲得數(shù)據(jù)庫,并給出了處理后的相關(guān)效果圖。第三,模式匹配:采用的是基于Ferns(蕨)分類器的方法。介紹訓(xùn)練集和測試樣本的劃分,分類器的原理,分析在虹膜匹配中所存在的優(yōu)點,并與支持向量機分類器進行了比較,還與常用的漢明距離法進行比較,給出三種方法的ROC性能曲線比較圖。為了對系統(tǒng)中本文提出的算法進行驗證,我們使用了中科院提供的CASIA和英國bath兩個虹膜數(shù)據(jù)庫共四萬多張樣本進行性能測試獲得了良好的效果。本文算法的操作平臺是在MATLAB軟件平臺上實現(xiàn)的。
[Abstract]:Biometric technology has been paid more and more attention by people and urgent demand in the market, and has been studied and applied continuously. Iris recognition is one of the techniques, which is very popular in human body biometrics. Because of its unique and superior physiological structure, such as uniqueness, stability, anti-counterfeiting, it has become an important means of personal identity identification in today's society, but also in national defense, security, electronic commerce, Finance and other fields have a wide range of application prospects. In this paper, the iris recognition system is deeply studied and analyzed, and a four-way scanning method is proposed to locate the inner and outer edges of the iris quickly and accurately, and the iris region and the module of the iris are normalized by bipolar coordinates, and the four-way scanning method is proposed. The improved two-dimensional Gabor filter feature extraction based on 25 directions and the improved Ferns classifier method for iris feature training and sample matching test. In this paper, the improved algorithm is mainly focused on the iris image pre-processing, feature extraction and pattern matching in three parts of the module research, the specific detailed description: first, the image of the iris image pre-processing, feature extraction and pattern matching module research. Pre-processing: iris localization is the key part of iris image preprocessing module. In this paper, a fast and accurate localization method based on four-way scanning method is used to locate the inner and outer edges of iris. Firstly, the edge image fusion based on the improved Canny operator and wavelet transform is used to obtain a sufficiently closed inner edge, then the pupil is segmented by four-way scanning, and the edge points are re-determined. The inner edge center radius is calculated, then the outer edge center radius is coarsely located according to the inner edge center radius. Finally, the accurate outer edge information is obtained according to the differential and integral operation, so as to accurately locate the inner and outer edge of the iris. In order to obtain more pure iris texture feature information, pre-processing includes image normalization and image enhancement before feature extraction. In this paper, bipolar coordinate normalization is used in normalization, and then module partition is carried out. Select more, more rich, pure iris texture information as far as possible, and use equalization enhancement processing. Second, feature extraction: the feature extraction module adopts the use of 2-D Gabor filter based on 5-5 direction improvement. The image features are extracted from the selected iris region, then the characteristics of the Gabor filter are re-analyzed and the parameters are set, the feature extraction and fusion of 25 texture directions are carried out, and the database is obtained by calculating and encoding the features according to the feature points. The relative effect diagram after treatment is given. Third, pattern matching: the method based on Ferns (fern) classifier is adopted. This paper introduces the classification of training set and test sample, the principle of classifier, analyzes the advantages of iris matching, compares it with support vector machine classifier, and compares it with hamming distance method in common use. The comparison diagrams of ROC performance curves of three methods are given. In order to verify the algorithm proposed in this paper, we use two iris databases, CASIA and bath, provided by the Chinese Academy of Sciences, to test the performance of more than 40,000 iris databases. The operation platform of this algorithm is implemented on the MATLAB software platform.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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