發(fā)布時(shí)間：2018-11-24 12:58

【摘要】：虹膜識(shí)別技術(shù)因虹膜的優(yōu)秀生物特性,在眾多的身份鑒別技術(shù)中脫穎而出,被列為最為安全與精確的身份鑒別技術(shù),具有廣闊的應(yīng)用前景與重要的學(xué)術(shù)研究?jī)r(jià)值。由于虹膜識(shí)別技術(shù)應(yīng)用環(huán)境的復(fù)雜性以及其涉及領(lǐng)域的廣泛性,其關(guān)鍵技術(shù)仍存在需要改進(jìn)之處。本文結(jié)合虹膜圖像自身的屬性以及常見(jiàn)的虹膜識(shí)別系統(tǒng)流程,對(duì)虹膜定位、虹膜特征提取、虹膜特征降維等相關(guān)技術(shù)進(jìn)行了系統(tǒng)的分析與研究。主要的工作如下：對(duì)Canny算子與Hou gh變換相結(jié)合的虹膜定位分割模型進(jìn)行研究,針對(duì)傳統(tǒng)Canny算子在邊緣信息提取時(shí)存在容易受噪聲影響以及需要手動(dòng)輸入閾值的缺陷,提出改進(jìn)的Canny算子：首先利用S ober算子計(jì)算像素點(diǎn)的梯度幅值和方向,然后通過(guò)雙線性插值求得梯度方向上的像素點(diǎn)幅值完成非極大值抑制,最后采用Otsu實(shí)現(xiàn)閾值自適應(yīng)選取。利用改進(jìn)的Canny算法與Hough變換結(jié)合實(shí)現(xiàn)對(duì)虹膜的定位,提升了定位的精確度。對(duì)定位后的虹膜圖像利用坐標(biāo)變換進(jìn)行歸一化處理并增強(qiáng),完成虹膜圖像預(yù)處理。針對(duì)基于2D-Gabor濾波器的虹膜特征提取得到的特征向量信息過(guò)于冗余的缺陷,提出了結(jié)合遺傳算法的虹膜特征篩選模型,該模型實(shí)現(xiàn)了對(duì)虹膜特征向量的有效降維。對(duì)基于標(biāo)準(zhǔn)遺傳算法實(shí)現(xiàn)的虹膜特征篩選模型進(jìn)行研究,針對(duì)其中存在的缺陷,結(jié)合粒子群算法的優(yōu)點(diǎn),提出改進(jìn)的遺傳算法：在整體框架中融入粒子群算法,同時(shí)設(shè)計(jì)具有自適應(yīng)性的遺傳算子。利用改進(jìn)的遺傳算法對(duì)特征向量進(jìn)行特征篩選,得到有效且低維的特征向量。最后采用移位Hamming距離差完成虹膜的分類,經(jīng)過(guò)特征篩選的低維特征向量得到了更高的匹配準(zhǔn)確率。本文實(shí)驗(yàn)的原始數(shù)據(jù)來(lái)自CASIA-V4-Thousand和CASIA-Iris-Lamp數(shù)據(jù)庫(kù),以衡量虹膜識(shí)別系統(tǒng)性能的評(píng)價(jià)標(biāo)準(zhǔn)False Accept Rate、False Reject Rate、Correct Recognition Rate、Equal Error Rate和Receiver Operating Characteristic Curve對(duì)系統(tǒng)進(jìn)行測(cè)試,驗(yàn)證了本文提出的改進(jìn)算法的有效性。
[Abstract]:Iris recognition technology has been listed as the safest and most accurate identification technology because of its excellent biological characteristics. It has broad application prospects and important academic research value. Due to the complexity of the application environment of iris recognition technology and its wide range of fields, the key technologies still need to be improved. In this paper, the iris location, iris feature extraction, iris feature dimensionality reduction and other related techniques are systematically analyzed and studied based on the iris image attributes and the common iris recognition system flow. The main work is as follows: the iris location segmentation model combined with Canny operator and Hou gh transform is studied. The traditional Canny operator is easy to be affected by noise and needs manual input threshold when extracting edge information. An improved Canny operator is proposed: firstly, S ober operator is used to calculate the gradient amplitude and direction of pixel points, then bilinear interpolation is used to obtain the non-maximum suppression of the pixel amplitude in the gradient direction. Finally, Otsu is used to adaptively select the threshold value. The improved Canny algorithm is combined with the Hough transform to realize the iris localization, which improves the accuracy of the location. The iris image is normalized and enhanced by coordinate transformation, and the iris image preprocessing is completed. Aiming at the defects of redundant information of iris feature extraction based on 2D-Gabor filter, an iris feature selection model combined with genetic algorithm is proposed, which can effectively reduce the dimension of iris feature vector. The iris feature screening model based on standard genetic algorithm is studied. Considering the shortcomings of particle swarm optimization algorithm and the advantages of particle swarm optimization algorithm, an improved genetic algorithm is proposed: integrating particle swarm optimization algorithm into the whole framework. At the same time, genetic operators with adaptability are designed. The improved genetic algorithm is used to screen the feature vectors and obtain the effective and low-dimensional feature vectors. Finally, the classification of iris is accomplished by shift Hamming distance difference, and the low dimensional feature vector which is filtered by feature can get higher matching accuracy. In this paper, the original data from CASIA-V4-Thousand and CASIA-Iris-Lamp database are used to measure the performance of iris recognition system. False Accept Rate,False Reject Rate,Correct Recognition Rate,Equal Error Rate and Receiver Operating Characteristic Curve are used to test the system. The effectiveness of the proposed improved algorithm is verified.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41;TP18

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