【摘要】：手指靜脈識(shí)別是一種新興的基于人體生理特點(diǎn)的生物特征識(shí)別技術(shù)。該識(shí)別技術(shù)利用手指掌側(cè)淺靜脈進(jìn)行身份識(shí)別。與其他生物特征識(shí)別一樣,手指靜脈識(shí)別有四個(gè)處理步驟,即圖像采集、預(yù)處理、特征提取及匹配。盡管手指靜脈識(shí)別的相關(guān)研究取得了一定的進(jìn)展,但每個(gè)處理步驟中仍存在一些問題尚待完善。比如,由于圖像采集過程中手指的隨機(jī)放置,數(shù)據(jù)庫(kù)中存在部分傾斜圖像,但此問題在現(xiàn)有的圖像預(yù)處理方法中沒有得到足夠的重視。同時(shí),現(xiàn)有感興趣區(qū)域提取方法大都是針對(duì)來(lái)自某一種設(shè)備采集的圖像設(shè)計(jì),當(dāng)處理來(lái)自其他設(shè)備采集的圖像時(shí),其分割性能將大打折扣。再如,在基于靜脈紋路的識(shí)別方法中,不完整的紋路和不魯棒的匹配方法使得識(shí)別性能仍有待提高。使用軟特征增強(qiáng)主特征區(qū)分性是生物特征識(shí)別領(lǐng)域中提高識(shí)別性能的一種有效方法,但在手指靜脈識(shí)別中該問題并沒有得到應(yīng)有的關(guān)注。另外,由于個(gè)體差異和采集設(shè)備性能不夠理想,手指靜脈圖像質(zhì)量存在較大差異,因此如何正確地評(píng)價(jià)圖像質(zhì)量也是手指靜脈識(shí)別中需要重視的問題。本文針對(duì)手指靜脈圖像感興趣區(qū)域提取、紋路特征提取、軟特征提取、圖像質(zhì)量評(píng)價(jià)等問題開展了深入研究,主要的工作和貢獻(xiàn)如下：(1)研究了一種基于滑動(dòng)窗口的手指靜脈感興趣區(qū)域提取方法。針對(duì)手指傾斜問題,提出了一種基于手指中線的傾斜校正方法。在圖像校正的基礎(chǔ)上,設(shè)計(jì)了一種基于滑動(dòng)窗口的方法檢測(cè)手指關(guān)節(jié)的位置,并基于此位置確定感興趣區(qū)域的高度。進(jìn)一步,由手指左右兩側(cè)邊界的內(nèi)切邊確定感興趣區(qū)域的寬度,至此就可以得到校正的感興趣區(qū)域。(2)提出了一種基于超像素分割的設(shè)備無(wú)關(guān)手指靜脈感興趣區(qū)域提取方法。多個(gè)不同設(shè)備采集的圖像在尺寸、背景灰度及噪聲的位置、面積、形狀等方面存在差異,這些差異使得現(xiàn)有的基于單設(shè)備的感興趣提取方法的性能大打折扣。但多設(shè)備采集的圖像存在以下兩方面的共性：(1)噪聲位于手指區(qū)域之外；(2)手指邊界處背景與手指區(qū)域灰度差異較大。同時(shí),超像素分割能夠?qū)⒕哂邢嗨苹叶戎档南噜徬袼貏澐值揭粋�(gè)塊中,這樣不僅噪聲被隔離,背景和手指區(qū)域也能夠被劃分到不同的塊中。因此,提出了一種基于超像素分割的感興趣區(qū)域提取方法,即從超像素邊界中追蹤出手指邊界,從而獲得感興趣區(qū)域。(3)研究了一種基于解剖學(xué)結(jié)構(gòu)分析的手指靜脈識(shí)別方法。現(xiàn)有基于靜脈紋路的識(shí)別方法性能不夠理想的主要原因之一是沒有對(duì)手指靜脈的解剖學(xué)結(jié)構(gòu)特點(diǎn)和成像特點(diǎn)進(jìn)行深入的分析和利用。該工作利用靜脈紋路具有谷形或半谷形橫切面的成像特點(diǎn),并結(jié)合靜脈紋路在解剖學(xué)上的方向性、寬度變化、連續(xù)性等特點(diǎn),提出了一種基于解剖學(xué)結(jié)構(gòu)分析的紋路提取方法。在匹配階段,為克服手指隨機(jī)放置造成的大尺度圖像平移問題,提出了基于靜脈主干的圖像校準(zhǔn)方法。同時(shí),為了克服小尺度紋路變形問題,提出了一種彈性匹配方法。為了全面地表征靜脈紋路,將圖像校準(zhǔn)過程中的靜脈主干重合度和靜脈網(wǎng)絡(luò)的彈性匹配得分進(jìn)行集成。(4)研究了一種結(jié)合軟特征的手指靜脈識(shí)別方法。該工作檢測(cè)手指遠(yuǎn)端關(guān)節(jié)寬度,并將其看作一種軟特征。為了融合關(guān)節(jié)寬度特征和靜脈特征,提出了三種框架,即融合框架、過濾框架、混合框架。實(shí)驗(yàn)證明,利用手指關(guān)節(jié)寬度作為軟特征能夠有效地增強(qiáng)手指靜脈特征的識(shí)別性能。(5)提出了一種基于支持向量機(jī)的手指靜脈圖像質(zhì)量評(píng)價(jià)方法。為了全面的表征圖像質(zhì)量,提出了三種質(zhì)量特征,即空域梯度特征、對(duì)比度特征及信息容量特征�？紤]到手指靜脈圖像質(zhì)量分類是小樣本非線性分類問題,支持向量機(jī)被用于該分類問題中。同時(shí),為了克服高、低質(zhì)量圖像間的類別不平衡問題,R-SMOTE技術(shù)被用來(lái)合成少數(shù)的低質(zhì)量圖像。
[Abstract]:Finger vein recognition is a new biological feature recognition technology based on human physiological characteristics. The identification technique uses the finger-palm-side superficial vein for identification. Like other biometric identification, finger vein recognition has four processing steps, image capture, pre-processing, feature extraction, and matching. Although some progress has been made in the research of finger vein recognition, there are still some problems in each processing step. For example, because of the random placement of the finger in the image acquisition process, there is a partial oblique image in the database, but this problem is not given enough attention in the existing image preprocessing method. At the same time, most of the existing region of interest extraction method is for image design from a certain kind of equipment, and when the image acquired from other equipment is processed, its segmentation performance will be greatly reduced. For example, in the vein-based recognition method, incomplete lines and non-robust matching methods make the identification performance still to be improved. The use of soft-feature to enhance the primary feature differentiation is an effective way to improve the recognition performance in the field of biometric identification, but the problem does not receive due attention in finger vein recognition. In addition, because that individual difference and the performance of the acquisition device are not ideal, there is a great difference in the quality of the vein image of the finger, so how to correctly evaluate the image quality is also a problem that needs to be paid attention to in the finger vein recognition. In this paper, a deep study is carried out on the problems of region extraction, texture feature extraction, soft feature extraction, image quality evaluation, etc. of finger vein image, and the main work and contribution are as follows: (1) A method for extracting a region of interest of a finger vein based on a sliding window is studied. In view of the problem of finger tilt, a method of tilt correction based on the mid-line of the finger is proposed. On the basis of image correction, a method for detecting the position of a finger joint based on a sliding window is designed, and the height of the region of interest is determined based on the position. Further, the width of the region of interest is determined by the internal trimming of the boundary between the left and right sides of the finger, so that a corrected region of interest can be obtained. And (2) a method for extracting a device-independent finger vein region of interest based on a super-pixel division is provided. The images acquired by a plurality of different devices are different in size, background gray level and noise location, area, shape, and the like, and the differences enable the existing single-device-based interest extraction method to be greatly reduced in performance. However, there are two aspects: (1) the noise is outside of the finger area; (2) the background of the finger boundary is larger than that of the finger area. At the same time, the super-pixel division can divide the adjacent pixels with similar gradation values into one block, so that not only the noise is isolated, but the background and the finger area can be divided into different blocks. Accordingly, a method for extracting a region of interest based on a super-pixel division is proposed, that is, a finger boundary is tracked from the super-pixel boundary to obtain a region of interest. (3) A method of finger vein recognition based on anatomical structure analysis was studied. One of the main causes of the existing recognition method based on the vein line is that the anatomical structure and the imaging characteristics of the finger vein are not analyzed and utilized in-depth. This work uses vein lines to have the features of the valley-shaped or half-valley cross-section, and combines the characteristics of the vein lines in the anatomy, such as the orientation, the width, the continuity and so on, and puts forward a pattern extraction method based on the analysis of the anatomical structure. In the matching stage, in order to solve the problem of large-scale image translation caused by random placement of fingers, an image calibration method based on a vein trunk is proposed. At the same time, in order to overcome the problem of small-scale grain deformation, an elastic matching method is proposed. In order to characterize the vein pattern comprehensively, the degree of the vein trunk and the elastic matching score of the vein network in the image calibration process are integrated. (4) A method of finger vein recognition combined with soft features was studied. This work detects the distal joint width of the finger and regards it as a soft feature. In ord to blend that width and vein characteristics of the joint, three framework, i. e., a fusion frame, a filter frame, and a mixing frame, are proposed. The experimental results show that the finger joint width is used as a soft feature to effectively enhance the recognition performance of finger vein features. And (5) a method for evaluating the quality of a finger vein image based on a support vector machine is proposed. In order to comprehensively characterize the image quality, three quality characteristics, namely, the spatial gradient characteristics, the contrast characteristics and the information capacity characteristics, are proposed. In view of that problem that the classification of the quality of the vein image of the finger is a non-linear classification of a small sample, a support vector machine is used in the classification problem. At the same time, in order to overcome the problem of the class imbalance between high and low quality images, the R-SMOTE technique is used to synthesize a small number of low quality images.
【學(xué)位授予單位】：山東大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41

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