本文選題：生物特征識(shí)別 + 手指靜脈識(shí)別　； 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：近年來,隨著信息技術(shù)的快速發(fā)展,信息安全受到人們?cè)絹碓蕉嗟年P(guān)注。生物特征識(shí)別技術(shù)作為保障信息安全的一種重要途徑,在信息安全領(lǐng)域具有不可替代的地位。與傳統(tǒng)的基于指紋、虹膜、人臉識(shí)別等生物特征識(shí)別技術(shù)相比,手指靜脈識(shí)別技術(shù)作為一種新的生物特征識(shí)別技術(shù),主要特點(diǎn)在于將手指上隱藏的靜脈血管特征作為識(shí)別的特征。除此以外,手指靜脈識(shí)別還具有以下三個(gè)特點(diǎn):活體識(shí)別、高安全性和非接觸性。由于以上種種優(yōu)勢(shì),手指靜脈識(shí)別已經(jīng)成為一個(gè)新的研究熱點(diǎn),因此指靜脈識(shí)別的相關(guān)研究具有重要的實(shí)際意義。本文首先介紹了指靜脈識(shí)別系統(tǒng)中的基本內(nèi)容和一些經(jīng)典算法;其次,考慮到指靜脈系統(tǒng)在采集圖像時(shí)手指的旋轉(zhuǎn)問題與系統(tǒng)安全性問題,本文提出了一種基于Radon變換和混沌加密的指靜脈識(shí)別算法;此外,本文研究了模糊金庫(kù)算法,并提出了一種基于指靜脈特征的模糊金庫(kù)方案。具體工作如下:1.研究了指靜脈識(shí)別算法的主要內(nèi)容,主要包括預(yù)處理、指靜脈圖像增強(qiáng)算法、紋路分割算法和生物特征識(shí)別算法中常用的性能分析指標(biāo)。2.提出了一種基于Radon變換和混沌加密的指靜脈識(shí)別算法。該算法首先對(duì)靜脈圖像進(jìn)行圖像增強(qiáng)、Niblack分割、填充、細(xì)化一系列操作得到指靜脈骨架圖像,然后根據(jù)Radon變換計(jì)算得到該指靜脈的基本方向,根據(jù)該基本方向?qū)χ胳o脈骨架進(jìn)行旋轉(zhuǎn)校正。在注冊(cè)時(shí),利用用戶輸入的密鑰生成Logistic混沌序列對(duì)指靜脈骨架特征進(jìn)行加密,并將加密后的特征圖像保存于特征數(shù)據(jù)庫(kù)中。在匹配時(shí),用戶需采集待驗(yàn)證指靜脈圖像并輸入密鑰,對(duì)采集的指靜脈經(jīng)過處理得到特征信息,再利用密鑰解密保存在數(shù)據(jù)庫(kù)中的指靜脈特征,計(jì)算兩特征矩陣間的Pearson相關(guān)系數(shù)進(jìn)行匹配。實(shí)驗(yàn)結(jié)果表明:該算法在不經(jīng)過大量模板訓(xùn)練的情況下也能取得較高的識(shí)別精度,且該系統(tǒng)的安全性更高。3.提出了一種適用于指靜脈的模糊金庫(kù)算法。該算法分為兩個(gè)階段:密鑰綁定和密鑰恢復(fù)。在密鑰綁定時(shí),使用細(xì)節(jié)點(diǎn)生成方法得到指靜脈細(xì)節(jié)點(diǎn),并定義了新的參數(shù)“長(zhǎng)度”,使用(x,y,l)描述每個(gè)細(xì)節(jié)點(diǎn),再添加大量的雜湊點(diǎn)對(duì)真實(shí)點(diǎn)進(jìn)行隱藏,對(duì)每個(gè)點(diǎn)(包括真實(shí)點(diǎn)和雜湊點(diǎn))進(jìn)行一系列數(shù)據(jù)處理和轉(zhuǎn)換后存入模糊金庫(kù)中,其中對(duì)真實(shí)點(diǎn)利用密鑰綁定多項(xiàng)式,而對(duì)雜湊點(diǎn)則利用隨機(jī)數(shù)計(jì)算。在恢復(fù)密鑰時(shí),通過比對(duì)查詢指靜脈中的細(xì)節(jié)點(diǎn)和模糊金庫(kù)中的點(diǎn)的坐標(biāo)信息,選擇相匹配的細(xì)節(jié)點(diǎn)并進(jìn)行相反計(jì)算,求得多項(xiàng)式并恢復(fù)出保護(hù)的密鑰。通過實(shí)驗(yàn)和分析表明:該模糊金庫(kù)算法具有良好的實(shí)用性和安全性。
[Abstract]:In recent years, with the rapid development of information technology, people pay more and more attention to information security. As an important way to guarantee information security, biometric identification plays an irreplaceable role in the field of information security. Compared with traditional biometrics such as fingerprint, iris and face recognition, finger vein recognition is a new biometric technology. The main feature is that the hidden veins on the finger as the recognition feature. In addition, finger vein recognition has three characteristics: in vivo recognition, high safety and non-contact. Because of the above advantages, finger vein recognition has become a new research hotspot, so the research of finger vein recognition has important practical significance. This paper first introduces the basic contents and some classical algorithms of digital vein recognition system. Secondly, considering the problem of finger rotation and system security when the digital vein system collects images, In this paper, a digital vein recognition algorithm based on Radon transform and chaotic encryption is proposed, in addition, the fuzzy vault algorithm is studied, and a fuzzy vault scheme based on finger vein features is proposed. The work is as follows: 1. In this paper, the main contents of digital vein recognition algorithm are studied, including preprocessing, enhancement algorithm of digital vein image, texture segmentation algorithm and biometric recognition algorithm. A digital vein recognition algorithm based on Radon transform and chaotic encryption is proposed. In this algorithm, the vein image is segmented, filled, and a series of operations are thinned to get the skeleton image of the digital vein. Then the basic direction of the digital vein is calculated according to the Radon transform. The digital venous skeleton was rotated and corrected according to the basic direction. At the time of registration, the Logistic chaotic sequence is generated by the user's key to encrypt the digital vein skeleton feature, and the encrypted feature image is stored in the feature database. When matching, the user needs to collect the image of the digital vein to be verified and input the key. After processing the collected digital vein, the characteristic information is obtained, and then the digital vein feature stored in the database is decrypted by using the key. The Pearson correlation coefficient between the two characteristic matrices is calculated to match. The experimental results show that the algorithm can achieve high recognition accuracy without a lot of template training, and the security of the system is higher. 3. A fuzzy vault algorithm for digital veins is proposed. The algorithm is divided into two stages: key binding and key recovery. In key binding, we use the detail generation method to get the minutiae of the finger vein, define the new parameter "length", describe each detail point by using a number of hash points, and hide the real point by adding a large number of hash points. Each point (including real point and hash point) is processed and converted and stored in the fuzzy vault, in which the key binding polynomial is used for the real point and the random number is used for the hash point. When the key is restored, by comparing the coordinate information between the point in the finger vein and the point in the fuzzy vault, the matching details are selected and the reverse calculation is carried out, and the polynomial is obtained and the protected key is restored. Experiments and analysis show that the fuzzy vault algorithm has good practicability and security.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41;TP309

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