纸上静态签名笔迹动态特征的提取和分析

目的建立一种新型的定量化签名笔迹鉴别技术。方法建立签名笔迹样本库，导入计算机后，提取签名笔迹的宽度，灰度和弧度等动态特征数据，进行时间归整后，比较签名动态特征之间的相关系数及其规律。结果同一人签名的动态特征之间相关系数高，而与代签签名、临摹签名和套摹签名均有显著差异。结论本研究开发和建立了一整套检测、提取和分析纸上签名笔迹动态特征的工具和方法，该方法能有效鉴别本人签名和非本人签名。     

中文签名笔迹动态特征研究

从物理学角度看,笔迹是书写工具在力的作用下在载体上形成的运动轨迹.作用力和速度的大小和变化构成了书写运动的动态特征.本文以签名笔迹为对象,研究签名笔迹的动态特征及其属性.实验结果表明动态特征能够作为认定书写人,鉴别摹仿签名的重要依据.     

中文签名笔迹动态特征研究

从物理学角度看,笔迹是书写工具在力的作用下在载体上形成的运动轨迹.作用力和速度的大小和变化构成了书写运动的动态特征.本文以签名笔迹为对象,研究签名笔迹的动态特征及其属性.实验结果表明:动态特征能够作为认定书写人,鉴别摹仿签名的重要依据.     

中文签名笔迹动态特征研究

从物理学角度看,笔迹是书写工具在力的作用下在载体上形成的运动轨迹.作用力和速度的大小和变化构成了书写运动的动态特征.本文以签名笔迹为对象,研究签名笔迹的动态特征及其属性.实验结果表明:动态特征能够作为认定书写人,鉴别摹仿签名的重要依据.     

中文签名笔迹动态特征研究

从物理学角度看,笔迹是书写工具在力的作用下在载体上形成的运动轨迹。作用力和速度的大小和变化构成了书写运动的动态特征。本文以签名笔迹为对象,研究签名笔迹的动态特征及其属性。实验结果表明：动态特征能够作为认定书写人,鉴别摹仿签名的重要依据。     

论签名笔迹的频谱分析方法

通过一种签名笔迹的频谱分析方法,并提取签名笔迹的压力、速度等动态特征数据,利用傅立叶变换原理,对特征数据进行频谱分析。实验研究发现,同一人的签名笔迹频谱图相似度高,且明显区别于他人的签名笔迹。签名笔迹的频谱分析方法可以直观、有效的鉴别签名笔迹。     

浅析摹仿签名笔迹鉴定

摹仿签名笔迹检验是笔迹检验的难点,认定签名摹仿人更是难中之难;笔者通过正确确定签名笔迹的真伪,从签名笔迹的概貌特征和细节特征之中运用辩证的观点正确认定摹仿人。     

浅谈对签名笔迹的检验

1 签名笔迹的特点 签名笔迹与其它笔迹相比较,有其独自的特点:1.签名笔迹的案由都是涉及到经济问题;2.签名笔迹字数少(两、三个字);3.签名笔迹的相同字、同名部首及同名笔画少;4.签名笔迹重复出现的笔迹特征少;5.签名笔迹可比性强的特征少。 2 对签名笔迹检验的方法 对签名笔迹检验的方法,是遵循同一认定的基本原理,对送检文字材料的笔迹特征同嫌疑人     

浅谈签名笔迹的独特特征

签名,是指享有某些权利或应尽有关义务的个人在有关文件上签署自己的姓名所形成的文字。签名和印文一起构成了文件及其所载内容的真实性和有效性的凭证。本文拟就签名笔迹独特特征的形成、表现形式以及对签名笔迹独特特征的寻找与运用做一些初步探讨。一、签名笔迹独特特征的形成本文所说的签名笔迹独特特征,是指非摹仿性的本质性的自然形成的独特的书写习惯的反映。签名笔迹的独特特征形成的因素很多,概括起来有以下几点:(一)心理因素;(二)生理因素;(三)审美观念和书写艺术素质不同的影响;(四)执笔方法不同的影响;(五)书写工具不同的影响。…     

再看摹仿签名笔迹鉴定

由于签名笔迹字数少、可摹仿性强的特点,长期以来,摹仿签名笔迹鉴定始终是笔迹鉴定的难点。笔者从不同类型的摹仿方法出发,讨论不同方法下摹仿签名笔迹的特点,总结其特征规律。同时要重视笔迹鉴定过程中了解相关案情,为识别是否存在摹仿现象提供线索。在此基础之上,对比摹仿签名笔迹特征的变化规律,并从摹仿签名笔迹特征反映构成方面系统性地进行鉴定,鉴别是否存在摹仿签名的情况。通过撰写此文,以期对摹仿签名笔迹鉴定实务具有借鉴意义。     

Forensic Analysis of Digital Dynamic Signatures: New Methods for Data Treatment and Feature Evaluation

This study explored digital dynamic signatures containing quantifiable dynamic data. The change in data content and nature necessitates the development of new data treatment approaches. A SignPad Omega digitizing tablet was used to assess measurement reproducibility, as well as within‐writer variation and the occurrence of correctly simulated features. Measurement reproducibility was found to be high except for pressure information. Within‐writer variation was found to be higher between days than on a same day. Occurrence of correct simulation was low for features such as signature size, trajectory length, and total signature time. Feature discrimination factors combining within‐writer variability and the occurrence of correctly simulated features were computed and show that signature size, trajectory length, and signature time are the features that perform the best for discriminating genuine from simulated signatures. A final experiment indicates that dynamic information can be used to create connections between simulation cases.     

笔迹动态特征研究

笔迹是通过书写运动形成的轨迹,由静态要素和动态要素所组成,承载着时间和空间信息。笔迹动态特征是客观存在的,它是书写人书写技能和书写习惯的外在表现,主要包括速度特征、笔力特征、脉冲特征、节奏特征等。对笔迹动态特征进行研究,多视域地挖掘和认识笔迹特征,可为笔迹检验鉴定意见提供更全面、更深层的支撑。     

汉英文字笔迹特征的比较研究

目的更深入的了解不同国家、地区,不同文种,不同文化背景下的笔迹鉴定工作者对特定文字笔迹鉴定认识的差别。方法以中英文为例,对汉字和英文的特点和笔迹特征进行比较研究。结论汉英文字的笔迹特征分类体系大体相似,并无质的差别,但是不同文字的特点决定了其笔迹特征取向与侧重、价值评断、个别特征的使用率,以及对同一人笔迹特征多样变化的认识上还是有所区别,各具特色。     

动态商标及其显著性认定

纸质媒介为主导的传统视觉生活是以静态的传统商标为主，但随着互联网的发展和普及以及电子商务的迅猛发展，非传统的动态商标逐渐受到国际社会的广泛关注。动态商标是以数字媒介为载体和动感设计为基础，是属于广义上的可视性商标，是符合绝大多数国家商标法对商标的规定。动态商标从本质上讲属于心理的认知，是消费者对商标所代表的有关商品或服务信息的评价。强大的视觉冲击效果和周而复始的广告宣传，使得动态商标在消费者的“认知网络”的信息组块中形成“结点”，从而使动态商标获得显著性。     

The contact area of static and dynamic footprints: Forensic implications

When found at crime scenes, footprints may be evidentially valuable and can assist with the identity of a perpetrator based on their features and/or measurements. Footprints can be either static (made while standing) or dynamic (made while walking). While extensive research has been performed on the linear measurements obtained from static and dynamic footprints, research on the comparisons between the contact area of static and dynamic footprints in the forensic context are limited. The present study compares the contact area of static and dynamic bare footprints to determine if statistically significant differences exist between the two. Static and dynamic footprints were obtained from a sample of randomly-selected 461 Jatt Sikh adults (230 males and 231 females) of Indian origin between the ages of 19 and 32 years. The footprint contact area was calculated from each footprint (excluding the toes) using a PedoGRID® sheet. No statistically significant differences were observed between the contact area of static and dynamic footprints for each foot among males and females. However, statistically significant differences between both the sexes were found in the footprint contact areas of both footprint types. The right dynamic footprint contact area was found to be the most predictive measurement for classifying and estimating sex from a footprint’s contact area. The study has implications in the analysis of footprints recovered from crime scenes.     

Analysis of the Effects and Relationship of Perceived Handwritten Signature's Size,Graphical Complexity,and Legibility with Dynamic Parameters for Forged and Genuine Samples

This article presents an analysis of handwritten signature dynamics belonging to two authentication groups, namely genuine and forged signature samples. Genuine signatures are initially classified based on their relative size, graphical complexity, and legibility as perceived by human examiners. A pool of dynamic features is then extracted for each signature sample in the two groups. A two‐way analysis of variance (ANOVA) is carried out to investigate the effects and the relationship between the perceived classifications and the authentication groups. Homogeneity of variance was ensured through Bartlett's test prior to ANOVA testing. The results demonstrated that among all the investigated dynamic features, pen pressure is the most distinctive which is significantly different for the two authentication groups as well as for the different perceived classifications. In addition, all the relationships investigated, namely authenticity group versus size, graphical complexity, and legibility, were found to be positive for pen pressure.     

Dynamic Characteristics of Signatures: Effects of Writer Style on Genuine and Simulated Signatures

The aims of this study were to determine if computer‐measured dynamic features (duration, size, velocity, jerk, and pen pressure) differ between genuine and simulated signatures. Sixty subjects (3 equal groups of 3 signature styles) each provided 10 naturally written (genuine) signatures. Each of these subjects then provided 15 simulations of each of three model signatures. The genuine (N = 600) and simulated (N = 2700) signatures were collected using a digitizing tablet. MovAlyzeR^® software was used to estimate kinematic parameters for each pen stroke. Stroke duration, velocity, and pen pressure were found to discriminate between genuine and simulated signatures regardless of the simulator's own style of signature or the style of signature being simulated. However, there was a significant interaction between style and condition for size and jerk (a measure of smoothness). The results of this study, based on quantitative analysis and dynamic handwriting features, indicate that the style of the simulator's own signature and the style of signature being simulated can impact the characteristics of handwriting movements for simulations. Writer style characteristics might therefore need to be taken into consideration as potentially significant when evaluating signature features with a view to forming opinions regarding authenticity.     

浅谈笔迹线条主动触觉在笔迹鉴定中的运用

笔迹是书写运动的轨迹,在观察静态特征的同时运用其动态特征,无疑可以提高笔迹鉴定的水平。但如何获取字迹的书写压力、书写速度和加速度等动态特征,目前还是个难题。笔迹心理学中笔迹线条主动触觉的分析是将笔迹书写时的压力与速度贯穿于字迹观察分析的始终,将其运用于笔迹鉴定,可在传统的笔迹鉴定二维平面字迹特征的视觉观察分析模式基础上,将视觉观察与触觉体验相结合,形成字迹形态二维平面特征与书写时笔向下的力度,和沿笔画书写的速度相结合的四维特征观察分析模式。     

A review on feature selection in mobile malware detection

The widespread use of mobile devices in comparison to personal computers has led to a new era of information exchange. The purchase trends of personal computers have started decreasing whereas the shipment of mobile devices is increasing. In addition, the increasing power of mobile devices along with portability characteristics has attracted the attention of users. Not only are such devices popular among users, but they are favorite targets of attackers. The number of mobile malware is rapidly on the rise with malicious activities, such as stealing users data, sending premium messages and making phone call to premium numbers that users have no knowledge. Numerous studies have developed methods to thwart such attacks. In order to develop an effective detection system, we have to select a subset of features from hundreds of available features. In this paper, we studied 100 research works published between 2010 and 2014 with the perspective of feature selection in mobile malware detection. We categorize available features into four groups, namely, static features, dynamic features, hybrid features and applications metadata. Additionally, we discuss datasets used in the recent research studies as well as analyzing evaluation measures utilized.