本文選題：生物特征識別 + 掌紋識別�。� 參考：《重慶師范大學》2017年碩士論文

【摘要】：隨著電子技術的快速成長,身份認證技術至關重要。掌紋識別技術,作為一種新興的生物特征識別技術。相對其它的,具有難以偽裝,比較穩(wěn)定、不具侵犯性以及難以隱藏和非接觸等特性。近年來掌紋識別技術在門禁、核電站、銀行系統(tǒng)還有快捷支付中的應用,說明關于掌紋識別技術的研究是一個比較前景和具有現(xiàn)實意義的研究課題。掌紋識別的關鍵就是在于提取掌紋的有效特征,并根據(jù)提取到的掌紋特征設計相應的分類器進行分類識別。本文提出的,基于SIFT和Gabor小波變換的兩類識別方法。主要從圖像區(qū)域分割,特征提取以及分類識別三個方面進行了研究。本文主要完成的研究工作如下。1、掌紋圖像的輪廓是通過順序統(tǒng)計濾波、二值化和形態(tài)學的處理得到的,然后將檢測到的角點中曲率最大的兩個點作為定位點來建立坐標,從而提取感興趣區(qū)域(ROI)。這種方法能有效分割圖像。2、設計了Gabor變換和KPCA結合的一種掌紋識別算法。首先提取掌紋圖像的四維四向變換,提取掌紋的紋理特征,將16組小波特征歸一化,然后,執(zhí)行核主成分分析(KPCA),并提取主成分,然后將主成分合并得到最終的向量特征,利用最近鄰分類器進行識別。相對于單一的Gabor和KPCA算法,該算法的識別率更高。3、設計了SIFT特征匹配的一種掌紋識別算法。該算法利用待識別SIFT掌紋特征關鍵點,與已有SIFT掌紋特征關鍵點的匹配統(tǒng)計特性,計算累計匹配點數(shù)量的數(shù)值和閾值來進行身份識別。在matlab環(huán)境下仿真,結果表明,算法的識別率很高。
[Abstract]:With the rapid development of electronic technology, identity authentication technology is very important. Palmprint recognition technology, as a new biometric recognition technology. Compared with others, it is difficult to disguise, stable, non-invasive, difficult to hide and non-contact. In recent years, the application of palmprint recognition technology in entrance guard, nuclear power station, banking system and fast payment system shows that the research on palmprint recognition technology is a relatively promising and practical research topic. The key of palmprint recognition is to extract the effective features of palmprint and design the corresponding classifier according to the extracted palmprint features. In this paper, two kinds of recognition methods based on SIFT and Gabor wavelet transform are proposed. In this paper, three aspects of image region segmentation, feature extraction and classification recognition are studied. The main work of this paper is as follows: 1. The contour of palmprint image is obtained by sequential statistical filtering, binarization and morphological processing, and then the two points with the largest curvature in the detected corner are taken as the positioning points to establish the coordinates. Thus the region of interest was extracted. This method can effectively segment image. 2. A palmprint recognition algorithm combining Gabor transform and KPCA is designed. First of all, we extract the four-dimensional four-direction transformation of palmprint image, extract the texture feature of palmprint, normalize the 16 groups of wavelet features, then perform kernel principal component analysis (KPCAA), extract the principal component, then merge the principal component to get the final vector feature. The nearest neighbor classifier is used for recognition. Compared with the single Gabor and KPCA algorithms, the recognition rate of this algorithm is higher. 3. A palmprint recognition algorithm for SIFT feature matching is designed. The algorithm uses the key points of SIFT palmprint feature to be identified and matches the statistical characteristics of the existing SIFT palmprint feature points to calculate the value and threshold of the cumulative number of matching points to carry out identification. Simulation results under matlab environment show that the recognition rate of the algorithm is very high.
【學位授予單位】：重慶師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

【參考文獻】

相關期刊論文 前10條

1 岑瑤;潘新;郜曉晶;劉霞;;基于MB-LBP和HOG的掌紋識別[J];計算機應用研究;2017年03期

2 王瑋婧;張雪鋒;;基于LGBP與Bloom濾波器的可撤銷掌紋模板生成方法[J];計算機應用研究;2017年02期

3 羅月童;趙蘭英;賈偉;顧靖;薛峰;;基于線特征韋伯局部描述子的掌紋識別[J];中國圖象圖形學報;2016年02期

4 趙志剛;吳鑫;張維忠;趙毅;洪丹楓;潘振寬;;融合水平梯度與局部信息強度的掌紋識別算法[J];計算機科學;2015年06期

5 吳婕;任江濤;;基于子空間特征融合的兩級掌紋識別算法[J];計算機工程與應用;2015年11期

6 尚麗;蘇品剛;淮文軍;;一種新的掌紋ROI圖像定位方法[J];激光與紅外;2012年07期

7 岳峰;左旺孟;張大鵬;;掌紋識別算法綜述[J];自動化學報;2010年03期

8 王艷華;張健;;基于形態(tài)學的掌紋圖像定位方法[J];遼寧工業(yè)大學學報(自然科學版);2009年04期

9 李文新,夏勝雄,張大鵬,許卓群;基于主線特征的雙向匹配的掌紋識別新方法[J];計算機研究與發(fā)展;2004年06期

10 黎明,嚴超華,劉高航;基于掌紋圖象分析的身份識別系統(tǒng)[J];中國圖象圖形學報;2000年02期

相關博士學位論文 前1條

1 郭秀梅;掌紋識別算法的研究[D];山東大學;2014年

，

本文編號：1976398