【摘要】：筆跡既包含著書寫者先天的生理特征,又受后天學(xué)習(xí)的影響,能在一定程度上反映書寫者的書寫習(xí)慣和生物特征。從筆跡中提取的信息可以用來判斷書寫者的性別、年齡和使用右手或者左手的習(xí)慣。其中,性別在書寫者筆跡風(fēng)格形成過程中的作用是不容忽視的。在取證分析和人口調(diào)查統(tǒng)計中,以性別將人群進(jìn)行劃分是非常有用的。確定書寫者的性別能夠縮小調(diào)查研究的范圍,并提高筆跡識別和筆跡驗證的效果。同時,結(jié)合性別與其他生物特征對案件分析有一定的啟發(fā)作用。本文著眼于基于筆跡的性別識別的研究。從輪廓特征、紋理特征和深度神經(jīng)網(wǎng)絡(luò)自動提取特征三個方面入手,實現(xiàn)了根據(jù)筆跡判斷書寫者性別的目標(biāo)。本文設(shè)計了鏈碼和邊界方向提取筆跡圖像的輪廓信息,利用SVM算法進(jìn)行分類,在IAM On-line數(shù)據(jù)庫上得到了71.2%的準(zhǔn)確率。本文研究了局部二值模式(LBP)的多種形式,以多尺度LBP提取筆跡圖像的紋理信息,通過實驗構(gòu)建了多尺度LBP特征并確定了合適的K值,同時使用KD樹分類,在IAM On-line數(shù)據(jù)庫上得到了73.25%的準(zhǔn)確率。在深度神經(jīng)網(wǎng)絡(luò)方面,本文分析了成熟網(wǎng)絡(luò)結(jié)構(gòu)的設(shè)計思路,在擴(kuò)充數(shù)據(jù)的基礎(chǔ)上,利用深度學(xué)習(xí)工具caffe搭建了包含七個卷積層和相應(yīng)功能層的卷積神經(jīng)網(wǎng)絡(luò),并使用多種技巧提高網(wǎng)絡(luò)性能,通過合理地設(shè)置參數(shù)和微調(diào),在IAM On-line數(shù)據(jù)庫上得到了76.17%的準(zhǔn)確率,這是該數(shù)據(jù)庫上筆跡性別識別的最高準(zhǔn)確率。
[Abstract]:Handwriting contains not only the inherent physiological characteristics of the writer, but also the influence of acquired learning, which can reflect the writing habits and biological characteristics of the writer to a certain extent. Information extracted from handwriting can be used to determine the sex, age, and habit of using the right or left hand of the writer. Gender plays an important role in the process of writing style formation. In forensic analysis and demographic statistics, it is very useful to divide the population by sex. Ascertaining the sex of the writer reduces the scope of research and improves the effectiveness of handwriting recognition and handwriting verification. At the same time, the combination of gender and other biological characteristics has a certain enlightening effect on case analysis. This paper focuses on the study of gender recognition based on handwriting. From three aspects of contour feature texture feature and depth neural network automatic extraction of features the goal of judging the sex of the writer according to handwriting is achieved. In this paper, we design chain code and boundary direction to extract the contour information of handwriting image and classify it with SVM algorithm. The accuracy rate is 71.2% in IAM On-line database. In this paper, the various forms of local binary mode (LBP) are studied. The texture information of handwriting image is extracted by multi-scale LBP. The multi-scale LBP feature is constructed through experiments and the appropriate K value is determined. At the same time, the KD tree is used to classify the texture information. The accuracy rate is 73.25% on IAM On-line database. On the aspect of depth neural network, this paper analyzes the design idea of mature network structure. Based on the extended data, a convolutional neural network including seven convolution layers and corresponding functional layers is constructed by using the depth learning tool caffe. By setting parameters and fine-tuning reasonably, the accuracy rate of 76.17% is obtained in IAM On-line database, which is the highest accuracy rate of handwriting gender recognition in this database.
【學(xué)位授予單位】：哈爾濱工業(yè)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9條

1 李昕;丁曉青;;基于改進(jìn)微結(jié)構(gòu)特征的筆跡鑒別[J];清華大學(xué)學(xué)報(自然科學(xué)版);2010年04期

2 李昕;丁曉青;彭良瑞;;一種基于微結(jié)構(gòu)特征的多文種文本無關(guān)筆跡鑒別方法[J];自動化學(xué)報;2009年09期

3 吳元敏;;基于Freeman鏈碼的圖像中多個區(qū)域面積的計算方法[J];計算機(jī)工程與應(yīng)用;2008年15期

4 厲小潤;趙光宙;趙遼英;;改進(jìn)的核直接Fisher描述分析與人臉識別[J];浙江大學(xué)學(xué)報(工學(xué)版);2008年04期

5 裘鎮(zhèn)宇;危輝;;基于Freeman鏈碼的邊緣跟蹤算法及直線段檢測[J];微型電腦應(yīng)用;2008年01期

6 張顯全;王繼軍;蔣聯(lián)源;;基于Freeman鏈碼的圓識別方法[J];計算機(jī)工程;2007年15期

7 劉宏,李錦濤,崔國勤,唐勝;基于SVM和紋理的筆跡鑒別方法[J];計算機(jī)輔助設(shè)計與圖形學(xué)學(xué)報;2003年12期

8 朱勇,譚鐵牛,王蘊紅;基于筆跡的身份鑒別[J];自動化學(xué)報;2001年02期

9 劉成林,劉迎建,戴汝為;基于多通道分解與匹配的筆跡鑒別研究[J];自動化學(xué)報;1997年01期

，

本文編號：2411109