本文關(guān)鍵詞： 離線簽名鑒別 改進(jìn)高斯濾波 改進(jìn)Ostu CNN 稀疏表示　出處：《西安科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著現(xiàn)代化信息技術(shù)突飛猛進(jìn)的發(fā)展,人們逐漸適應(yīng)并習(xí)慣了由各種信息網(wǎng)絡(luò)組成的全面數(shù)字信息化的生活方式。在此大背景下,身份認(rèn)證已成為人們的日常生活中不可或缺的一部分。目前簽名是很多領(lǐng)域中實際使用率最高的一種生物特征,人們對將簽名用于虛擬經(jīng)濟(jì)的移動支付上的渴望也逐漸加深,近幾年來對簽名鑒別系統(tǒng)的研究也已成為熱點。本文主要研究的是離線手寫簽名的分類鑒別,主要的研究過程可以分為四個階段:簽名圖像樣本采集階段、簽名圖像樣本預(yù)處理階段、簽名圖像樣本特征提取階段和不同方法分類鑒別階段。本文主要的創(chuàng)新點如下:(1)為更好的對簽名圖像進(jìn)行平滑濾波,克服傳統(tǒng)高斯濾波的不足,更好的表現(xiàn)其尺度不變性和旋轉(zhuǎn)對稱性,本文基于傳統(tǒng)高斯濾波提出了一種改進(jìn)的高斯濾波方法,實驗結(jié)果表明,改進(jìn)高斯濾波進(jìn)行平滑去噪的效果比其他三種方法好,說明提出的改進(jìn)方法是可行的。(2)為更好的對簽名圖像進(jìn)行二值分割,自適應(yīng)地找到更合適的閾值,在Ostu的基礎(chǔ)上提出了改進(jìn)。通過簽名圖像二值化實驗,結(jié)果證明改進(jìn)Ostu二值化的效果比其他三種方法好,說明提出的改進(jìn)方法是可行的。(3)為了能更好的用最少的、最合適的特征去表示一個簽名的全部特征,本文利用CNN結(jié)構(gòu)在訓(xùn)練參數(shù)的過程中會自己根據(jù)數(shù)據(jù)學(xué)習(xí)并表示簽名特征的特點,提出了一種基于CNN特征提取和稀疏表示的融合算法進(jìn)行迭代和分類決策,實驗結(jié)果表明此融合算法得到的測試集分類準(zhǔn)確率平均提高到了 98.2054%,是四種算法中分類準(zhǔn)確率最高的,說明此算法是可行的。系統(tǒng)實驗結(jié)果表明,本文設(shè)計并實現(xiàn)的離線手寫簽名分類鑒別系統(tǒng)是穩(wěn)定可靠的,為在線簽名分類鑒別系統(tǒng)的研究和在移動設(shè)備的應(yīng)用上提供了一個良好的方向。
[Abstract]:With the rapid development of modern information technology, people gradually adapt to and get used to the comprehensive digital information life style composed of all kinds of information networks. Identity authentication has become an indispensable part of people's daily life. At present, signature is one of the most widely used biometric features in many fields, and people's desire to use it for mobile payment in virtual economy is becoming more and more serious. In recent years, the research on signature authentication system has also become a hot topic. This paper mainly studies the classification and authentication of off-line handwritten signatures. The main research process can be divided into four stages: the phase of signature image sample collection. Signature image sample preprocessing stage, signature image sample feature extraction stage and different method classification stage. The main innovation of this paper is as follows: 1) in order to better smooth filter signature image, overcome the shortcomings of traditional Gao Si filter. The scale invariance and rotation symmetry are better represented. Based on the traditional Gao Si filter, this paper puts forward an improved Gao Si filtering method. The experimental results show that the improved Gao Si filter is more effective than the other three methods in smoothing noise removal. It is shown that the improved method is feasible. In order to better binary segmentation of signature image and find more suitable threshold adaptively, an improvement is proposed on the basis of Ostu. The results show that the effect of improved Ostu binarization is better than the other three methods, which shows that the proposed improved method is feasible.) in order to better use the least and most appropriate features to represent all the features of a signature, In this paper, a fusion algorithm based on CNN feature extraction and sparse representation is proposed to make iterative and classification decision based on the characteristics of data learning and signature feature representation in the process of training parameters by CNN structure. The experimental results show that the average classification accuracy of the test set obtained by the fusion algorithm is 98.2054, which is the highest among the four algorithms, which shows that the algorithm is feasible. The off-line handwritten signature classification and authentication system designed and implemented in this paper is stable and reliable, which provides a good direction for the research of online signature classification authentication system and its application in mobile devices.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 黃劍冰;李奇;;復(fù)雜光照環(huán)境下的灰度圖像二值化算法的研究及應(yīng)用[J];冶金自動化;2017年02期

2 穆s，

本文編號：1508329