印刷图文特征测量系统的研究

通过对选择的50余款国内外图像、图形分析处理软件、图像测量软件和测量工具系统的比较研究,根据文件鉴定中印刷文件鉴定的实践需求,通过有效的整合,建立了一套适用于印刷文件鉴定实际需求的,能有效对印刷图文特征进行比较测量和精确测量及量化分析的技术平台,并对印刷图文特征的量化分析在印刷文件鉴定中的应用做了初步探索。     

基于图像识别的橡胶鞋底Schallamach波自动计数方法

在犯罪现场中，鞋印是一种常见的痕迹物证。Schallamach波是橡胶鞋底上的一种磨损痕迹特征，对鞋印的检验鉴定具有一定的实际意义。目前对Schallamach波纹线采用人工统计，耗时耗力，因此本文利用图像识别提出一种自动计数方法。首先对图像进行二值化处理实现纹线与背景的初步分离，然后对图像进行形态学处理，利用迭代腐蚀膨胀操作分离粘合纹线，消除杂点，填充孔洞。通过Zhang-Suen(ZS)细化操作提取纹线骨架，在剔除干扰纹线后，每隔10个像素进行一次计数，得到的平均数作为全图纹线数目。使用不同密度图像进行验证，结果表明，在低、中密度时误差均控制在1条左右，计数时间均为1 s左右。本文方法能够对Schallamach纹线高效计数，与人工计数相比耗时显著降低，在面对大范围统计时将节约大量时间成本。     

图像真伪鉴定内涵

伪造图像检验日益成为一个研究和应用的热点。但是目前针对图像的真伪及其鉴定方法还没有较清晰的界定。尤其在物证鉴定领域,在方法相对缺乏,而实际需求日趋增长的情况下,如何有效地对具体案件检材进行检验是我们在司法活动中遇到的巨大挑战。本文根据作者在相关检验中所遇问题做出的一些思考,结合工作实际,对图像真伪鉴定的内涵进行探讨。     

拖鞋足迹特征变化规律的探讨

本文通过对30双不同鞋号的拖鞋进行观测研究,初步摸索出拖鞋足迹特征变化的规律性和分析、鉴定方法。     

图像降噪和增强对人脸识别系统识别性能的影响

目的研究图像降噪和图像增强方法对人脸识别系统识别性能的影响,以期为人脸识别系统应用过程中的图像处理方法选取提供理论指导和技术方案。方法收集33起人像鉴定领域实际案例中的人脸图像素材,研究以高斯滤波和小波变换为代表的图像降噪技术以及具有边缘保持和小波变换特性的单帧图像超分辨率增强技术对人脸识别系统识别性能的影响,并对不同图像处理方法对人脸识别性能的影响进行量化比较分析。结果本文研究的图像降噪技术均显著提高了人脸识别系统的识别准确性,而图像增强技术虽然提高了人脸图像显示效果,但对人脸识别系统的识别性能无正向促进作用。此外,高斯模糊图像处理的图像降噪方法虽然简单,但与本文研究的其他方法比较,其在人脸识别系统识别性能改善方面效果最显著。结论人脸图像质量对人脸识别系统的识别性能具有显著影响,可以通过图像处理技术改善人脸图像质量进而提高人脸识别系统的识别准确性。其中,图像降噪处理可以显著提高人脸识别系统的识别性能,且比图像增强技术更适合于实际人像鉴定应用中的人脸识别系统识别性能增强。     

笔迹鉴定的量化及统计学应用方法综述

统计学方法在笔迹鉴定领域的研究中越来越受到重视,采取有效的统计学方法对笔迹的特征进行客观量化,并对特征数据进行合理的分析,不仅可以为笔迹鉴定结论提供强有力的理论依据,更可以深入挖掘复杂数据背后的信息。本文梳理了笔迹特征量化和特征数据处理中几种常用统计学方法,介绍了它们的原理、应用以及最新进展,并对笔迹鉴定中统计学方法进行了展望。     

统计分析法对文书作者推定研究中的文体特征提取

文章作者归属(authorshipattribution)问题是从古至今引人注目的研究领域之一。在IT不断发展的现代社会,用文书处理软件制作的文书作者鉴定、制作时间鉴定也成为司法鉴定研究领域的新课题。目前,在欧美、日本等国,文体计量分析已运用于刑事侦查和司法证据鉴定中的文书作者认定或推定,以及文书制作时间推定中。我国运用统计学方法对文体进行计量分析的研究报导较少,运用于司法鉴定领域还是空白。本文就利用统计学对文体计量分析在文书作者推定中的运用作了介绍及初步论述,供大家参考。     

听辨在声纹鉴定中的作用

目的对言语特征进行研究,扩大声纹鉴定的应用范围。方法分析当前声纹鉴定的技术方法,汇总听辨结合的几种方法,找出听辨结合的关键点。结果对以往的鉴定经验和教训进行归纳总结,改进现有的鉴定模式和理念,使其贴近实战、贴近案件需求。     

指纹三级特征的研究现状及展望

现场指纹质量的变化，鉴定标准的多样化以及错案带来的挑战，使得指纹鉴定人员把目光从宏观的指纹特征转向微观的指纹特征。随着指纹显现提取技术的不断进步，三级特征逐渐出现在指纹鉴定人员的视野并被理解和应用，对三级特征的研究也处于活跃状态。本文从指纹三级特征的基本属性、可靠性、自动识别以及鉴定属性四个方面，对当前国内外指纹三级特征的研究现状进行全面深入的分析和归纳，明确了三级特征研究的进程，并对未来三级特征的研究方向进行了探讨，为三级特征融入指纹证据评价提供了一定的借鉴。     

基于深度学习的指纹自动识别技术

本文从学科领域入手,对指纹自动识别技术在发展过程中受人工智能技术影响所产生的新变化进行简述。指纹识别技术作为一种计算机应用技术,其发展与计算机科学的新技术密切相关。人工智能技术,特别是基于深度学习的图像技术的发展使指纹识别算法开启了全新的模式。本文将人工智能在指纹领域的发展分成三个阶段,并对当前所处的第二阶段的发展趋势进行了分析。基于深度学习的指纹识别技术使用图像特征而不是传统细节点特征的方式,改变了法庭科学领域对指纹识别的认知。本文重点对深度学习技术在指纹识别方面的应用模式和典型的技术方法进行了论述,给出了基于深度学习的指纹识别技术方案图,对技术方案中的网络模型设计等重要步骤逐一进行了说明,提出了图像处理、降维等几个需要重点攻坚的技术环节。对现有的可为指纹识别借鉴使用的深度网络模型进行了介绍,如:卷积神经网络、自编码器网络。最后对人工智能指纹识别算法与传统算法的性能进行了对比。     

Digital camera identification using PRNU: A feature based approach

Source camera identification is one of the emerging field in digital image forensics, which aims at identifying the source camera used for capturing the given image. The technique uses photo response non-uniformity (PRNU) noise as a camera fingerprint, as it is found to be one of the unique characteristic which is capable of distinguishing the images even if they are captured from similar cameras. Most of the existing PRNU based approaches are very sensitive to the random noise components existing in the estimated PRNU, and also they are not robust when some simple manipulations are performed on the images. Hence a new feature based approach of PRNU is proposed for the source camera identification by choosing the features which are robust for image manipulations. The PRNU noise is extracted from the images using wavelet based denoising method and is represented by higher order wavelet statistics (HOWS), which are invariant features for image manipulations and geometric variations. The features are fed to support vector machine classifiers to identify the originating source camera for the given image and the results have been verified by performing ten-fold cross validation technique. The experiments have been carried out using the images captured from various cell phone cameras and it demonstrated that the proposed algorithm is capable of identifying the source camera of the given image with good accuracy. The developed technique can be used for differentiating the images, even if they are captured from similar cameras, which belongs to same make and model. The analysis have also showed that the proposed technique remains robust even if the images are subjected to simple manipulations or geometric variations.     

A symmetry perceiving adaptive neural network and facial image recognition

The paper deals with the forensic problem of comparing nearly from view and facial images for personal identification. The human recognition process for such problems, is primarily based on both holistic as well as feature-wise symmetry perception aided by subjective analysis for detecting ill-defined features. It has been attempted to approach the modelling of such a process by designing a robust symmetry perceiving adaptive neural network. The pair of images to be compared should be presented to the proposed neural network (NN) as source (input) and target images. The NN learns about the symmetry between the pair of images by analysing examples of associated feature pairs belonging to the source and the target images. In order to prepare a paired example of associated features for training purpose, when we select one particular feature on the source image as a unique pixel, we must associate it with the corresponding feature on the target image also. But, in practice, it is not always possible to fix the latter feature also as a unique pixel due to pictorial ambiguity. The robust or fault tolerant NN takes care of such a situation and allows fixing the associated target feature as a rectangular array of pixels, rather than fixing it as a unique pixel, which is pretty difficult to be done with certainty. From such a pair of sets of associated features, the NN searches out proper locations of the target features from the sets of ambiguous target features by a fuzzy analysis during its learning. If any of target features, searched out by the NN, lies outside the prespecified zone, the training of the NN is unsuccessful. This amounts to non-existence of symmetry between the pair of images and confirms non-identity. In case of a successful training, the NN gets adapted with appropriate symmetry relation between the pair of images and when the source image is input to the trained NN, it responds by outputting a processed source image which is superimposable over the target images and identity may subsequently be established by examining detailed matching in machine-made superimposed/composite images which are also suitable for presentation before the court. The performance of the proposed NN has been tested with various cases including simulated ones and it is hoped to serve as a working tool of forensic anthropologists.     

An economised craniofacial identification system

It has been attempted to develop an economised craniofacial identification system, as a special automated version of photo/video superimposition technique, that can deal with common cases of personal identification with the aid of a skull and a nearly front view face photograph of the suspected victim.The proposed method is economic in respect of (i) cost of hardware configuration, (ii) processing time as well as (iii) manual labour involved. Over and above, it has got a capability to take care of ambiguities due to soft tissue thickness during the selection of facial features, which is a part of the procedure.In order to reconstruct a 2-D cranial image, superimposable over the facial one, the new method does not need any reconstruction of a digitised 3-D cranial image. It works simply by a suitable segment-wise processing of a 2-D cranial image with the aid of the symmetry perceiving adaptive neuronet (SPAN), that has recently been introduced in connection with nearly front view facial image recognition. The final comparison of the facial and the superimposable cranial images is as versatile as the same for facial image recognition by SPAN.A practical application of this extended version of SPAN has been demonstrated in the present paper.     

Multi-resolution image fusion technique and its application to forensic science

Image fusion is a process of combining two or more images into an image. It can extract features from source images, and provide more information than one image can. Multi-resolution analysis plays an important role in image processing, it provides a technique to decompose an image and extract information from coarse to fine scales. In some practical forensic examinations (such as the cartridge image check), we cannot obtain all information from just one image; on the contrary, we need information from images with difference light sources (or light ways). In this paper, we apply an image fusion method based on multi-resolution analysis to forensic science. Synthetic and real images (such as images from closed-up photography and flash photography) are used to show the capability of the multi-resolution image fusion technique.     

Multi-resolution image fusion technique and its application to forensic science

Image fusion is a process of combining two or more images into an image. It can extract features from source images, and provide more information than one image can. Multi-resolution analysis plays an important role in image processing, it provides a technique to decompose an image and extract information from coarse to fine scales. In some practical forensic examinations (such as the cartridge image check), we cannot obtain all information from just one image; on the contrary, we need information from images with difference light sources (or light ways). In this paper, we apply an image fusion method based on multi-resolution analysis to forensic science. Synthetic and real images (such as images from closed-up photography and flash photography) are used to show the capability of the multi-resolution image fusion technique.     

Post-mortem Iris Decomposition and its Dynamics in Morgue Conditions

With increasing interest in employing iris biometrics as a forensic tool for identification by investigation authorities, there is a need for a thorough examination and understanding of postmortem decomposition processes that take place within the human eyeball, especially the iris. This can prove useful for fast and accurate matching of antemortem with postmortem data acquired at crime scenes or mass casualties, as well as for ensuring correct dispatching of bodies from the incident scene to a mortuary or funeral homes. Following these needs of forensic community, this paper offers an analysis of the coarse effects of eyeball decay done from a perspective of automatic iris recognition. We analyze postmortem iris images acquired for a subject with a very long postmortem observation time horizon (34 days), in both visible light and near-infrared light (860 nm), as the latter wavelength is used in commercial iris recognition systems. Conclusions and suggestions are provided that may aid forensic examiners in successfully utilizing iris patterns in postmortem identification of deceased subjects. Initial guidelines regarding the imaging process, types of illumination, and resolution are also given, together with expectations with respect to the iris features decomposition rates. Visible iris features possible for human, expert-based matching persists even up to 407 h postmortem, and near-infrared illumination is suggested for better mitigation of corneal opacity while imaging cadaver eyes (Post-mortem iris decomposition and its dynamics in morgue conditions. ArXiv pre-print, 2019).     

Hair-MAP: a prototype automated system for forensic hair comparison and analysis

This paper demonstrates the feasibility of the automation of forensic hair analysis and comparison task using neural network explanation systems (NNESs). Our system takes as input microscopic images of two hairs and produces a classification decision as to whether or not the hairs came from the same person. Hair images were captured using a NEXTDimension video board in a NEXTDimension color turbo computer, connected to a video camera. Image processing was done on an SGI indigo workstation. Each image is segmented into a number of pieces appropriate for classification of different features. A variety of image processing techniques are used to enhance this information. Use of wavelet analysis and the Haralick texture algorithm to pre-process data has allowed us to compress large amounts of data into smaller, yet representative data. Neural networks are then used for feature classification. Finally, statistical tests determine the degree of match between the resulting collection of hair feature vectors. An important issue in automation of any task in criminal investigations is the reliability and understandability of the resulting system. To address this concern, we have developed methods to facilitate explanation of neural network's behavior using a decision tree. The system was able to achieve a performance of 83% hair match accuracy, using 5 of the 21 morphological characteristics used by experts. This shows promise for the usefulness of a fuller scale system. While an automated system would not replace the expert, it would make the task easier by providing a means for pre-processing the large amount of data with which the expert must contend.     

Classification algorithm using halftone features of counterfeit bills and CNN

With recent advancements in image processing and printing technology, home printers have improved in performance and grown more widespread. As such, they have been increasingly used in counterfeiting and forgery. Most counterfeit bills in Korea have been created using home scanners and printers. The identification of printer model is thus necessary to rapidly track down criminals and solve crimes. Household printers can be largely divided into inkjet and laser printers. These two types of printers print halftone textures instead of continuous images. This study proposed a technique of printer classification based on halftone textures that can be observed in printed documents. Since halftone textures are expressed as periodic lattices, the images were transformed via FFT, which is highly effective at expressing periodicity. ResNet, known for its superior gradient flow, was used for training. The experiment was conducted on 12 color laser jets and 2 inkjets. Scans of bills printed by each printer were used, and halftone texture analysis was performed on these images for printer model classification. Each image was cropped into several parts; one of the cropped parts was analyzed. The analysis showed that laser printers could be 100% distinguished from inkjet printers. An accuracy of 98.44% was achieved in make classification. When 50 cropped images were used instead of a single image, the technique achieved 100% accuracy in model classification. The proposed technique is non-destructive; it offers high accessibility and efficiency as it can be performed using a scanner alone, without requiring additional optical equipment.     

Computer‐aided Dental Identification: An Objective Method for Assessment of Radiographic Image Similarity*

Abstract: A pilot study evaluated a computer‐based method for comparing digital dental images, utilizing a registration algorithm to correct for variations in projection geometry between images prior to a subtraction analysis. A numerical assessment of similarity was generated for pairs of images. Using well‐controlled laboratory settings, the method was evaluated as to its ability to identify the correct specimen with positive results. A subsequent clinical study examined longitudinal radiographic examinations of selected anatomical areas on 47 patients, analyzing the computer‐based method in making the correct identification based upon a threshold level of similarity. The results showed that at a threshold of 0.855, there were two false negative and two false positive identifications out of 957 analyses. Based on these initial findings, 25 dental records having two sets of full mouth series of radiographs were selected. The radiographs were digitized and grouped into six anatomical regions. The more recent set of films served as postmortem images. Each postmortem image was analyzed against all other images within the region. Images were registered to correct for differences in projection geometry prior to analysis. An area of interest was selected to assess image similarity. Analysis of variance was used to determine that there was a significant difference between images from the same individual and those from different individuals. Results showed that the threshold level of concordance will vary with the anatomical region of the mouth examined. This method may provide the most objective and reliable method for postmortem dental identification using intra‐oral images.