Matlab用于头颅CT数据重建和测量1例

计算机颅骨面貌复原是目前法医人类学研究的热点之一。而该研究中颅骨特征和五官特征之间的相关性研究不多,要研究颅骨和五官的相关性,首先要建立大样本的活体颅骨和面貌的三维测量数据库。利用活体薄层CT数据进行研究,既可以重建颅骨,也可以重建面貌,因而可以更好的研究其相关性。     

颅像复原技术的发展

从解剖学家Schaaffhasen于1877年首次提出利用颅骨复原远古人类面貌的设想起,百余年来人们一直在探索着将这一有点神秘的技术应用于刑事司法实践,为寻找颅骨的身源人及颅骨的身源鉴定提供帮助。利用颅骨复原身源人面貌在刑事侦查中的重要意义是显而易见的。涉及颅骨(白骨)的案件,多为碎尸或杀人抛尸、掩埋的案件,发现时尸骨多是面目全非,难以进行面貌识别。可以说,确定身源是侦破这类案件的关键。目前,在刑事科学领域,确定颅骨身源的方法有两种:颅像复原法和颅像重合技术。在颅像复原研究困难重重的情况下,颅像重合技术研究却取得了可喜的成果。1987年,铁岭市     

利用计算机进行颅像复原初探

随着无名尸身源查找工作的迫切要求，颅骨面貌复原技术越来越引起刑事技术部门的重视。笔者主要从事刑事记忆画像工作，曾多次利用计算机为无名尸进行颅像面貌复原，积累了点滴经验，介绍如下。     

受毁损尸体模拟画像面貌复原技术探讨

侦查工作中经常会遇到受毁损的无名尸体,此时寻找尸源无疑是一项首要的任务。近年来,随着计算机技术的突飞猛进,面貌复原已经由传统的二维描绘、三维雕塑向计算机颅骨面貌复原、计算机模拟组合像描绘等方面推进[1]。然而,由于作者可结合办案经验、自身绘画素养以及对人面貌的直觉进行描绘,因此传统画像具有电脑无法达到的优点[2,3],笔者近年来多次受邀对受毁损的无名尸体进行面貌复原,结合检案实践谈点体会。1受毁损尸体成因及分类尸体受毁损有自然形成、意外事故形成、人为因素形成等三种类型:自然形成的情况有尸体腐败、尸体干燥(干尸)、…     

人类颅骨面貌复原研究现状

<正> 颅骨面貌复原(facial reconstructionfrom skull)是人类学的一项重要内容,是古人类学作为再现古代人类,体质人类学作为再现多种民族和历史名人,法医人类学作为个体识别等的重要手段。目前,颅骨面貌复原大体分为以下几个步骤:(1)根据颅骨判别年龄、性别和种族;(2)翻制颅骨的石膏模型;(3)在颅骨石膏模型上按收集或测得的面部软组织厚度用软塑胶     

数字图像复原技术的原理及在刑事技术中的应用

目的介绍数字图像处理技术在刑事案件复原的应用,促进该技术的发展。方法利用数字算法、滤波、几何运算等计算机图像处理技术进行复原处理。结果对案件的隐含、残缺、微弱信息,模糊图像、变形物证复原及特征显现。结论刑事数字图像复原技术在刑事技术领域有了广阔的应用前景。     

疤痕致人体容貌毁损的鉴定

人体容貌是指颜面部皮肤和五官的外露部分，其范围一般是指前额发际下，两耳根前与下颌了缘之间的区域，包括额部、眼眶部、鼻部、口唇、颌部、颧部、颊部、腮腺咬肛L部和耳廓。当人体颖而受到各种伤害，使正常的皮肤。组织、器官发生形态结构改变，并产生难以复原的损伤，致使容貌显著变形、丑陋或者功能障碍，称之为容貌毁损（指构成重伤）。容貌毁损从形态学和功能上可分为：面部五官缺损而致毁容；神经麻痹引起的毁容；骨折畸形愈合所致的毁容和皮肤疤痕所致的毁容。其中，颜面皮肤疤痕所致的毁容，多由于锐器的切割、砍、刺创愈后，亦…     

人体毛发尖部形态的扫描电镜研究

采用扫描电镜观察人体毛发尖部微观形态特征，总结归纳了人体主要几个部位毛发尖部形态的八种类型，探讨各类毛尖形状其形成的可能条件、分布的规律性以及修剪后端部的变化情况，为研究人体毛发生长机理、法庭科学的毛发鉴定工作提供依据。     

藏在头骨中的面容

“当无名尸体案的一切侦破努力都成为泡影时,或许头骨面部重建技术便是可以利用的最后一张王牌了。”——美国颅骨面貌复原专家盖特里夫     

个人信息匿名化的理论基础与制度建构

匿名化技术作为兼顾数据利用和数据安全的技术始终难以真正落地,原因在于《个人信息保护法》第73条对“匿名化”概念设置了“无法识别”和“不能复原”两个限定条件,这一苛刻要求使得匿名化制度处于“存而不用”的状态。虽然匿名化处理无法实现完全匿名已成共识,但是对于相对匿名的判断标准以及重新识别风险的限定范围始终存有较大争议。现阶段匿名化制度的理论探讨普遍忽视了对该项制度的理论基础探究,而将解决思路限定在个体权益保护的范畴。在数据关系理论范式下,这种信息要素关联性指向的是横向数据关系层面的群体性特征,而这种群体性特征恰恰也是数据经济价值的关键所在。因此,匿名化制度的功能定位应当从单纯地保障个体信息私密性转变为在保障数据关联经济价值的基础上降低重新识别风险。在制度建构层面,匿名化信息所要求的“不能复原”应当被解释为经由事前的风险评估,复原的技术难度和时间成本远超一般主体所能接受的范围。     

Facial Approximation—From Facial Reconstruction Synonym to Face Prediction Paradigm

Facial approximation was first proposed as a synonym for facial reconstruction in 1987 due to dissatisfaction with the connotations the latter label held. Since its debut, facial approximation's identity has morphed as anomalies in face prediction have accumulated. Now underpinned by differences in what problems are thought to count as legitimate, facial approximation can no longer be considered a synonym for, or subclass of, facial reconstruction. Instead, two competing paradigms of face prediction have emerged, namely: facial approximation and facial reconstruction. This paper shines a Kuhnian lens across the discipline of face prediction to comprehensively review these developments and outlines the distinguishing features between the two paradigms.     

Anthropological facial ‘reconstruction’ – recognizing the fallacies, ‘unembracing’ the errors, and realizing method limits

Despite being practiced for over the last 200 years, facial approximation methods remain in their infancy as the soft tissue prediction methods employed have not been tested and justified. Scientific testing is the only way forward and much of it is needed. The lack of systematic scientific tests in the past has enabled many misleading notions to become established. Many of these notions appear to have arisen and been sustained as a result of practitioner biases--this is clearly evident even in the name commonly used to describe the method of building faces from skulls, for "facial reconstruction" implies everything the method is not, e.g., technical/scientific, exact, and credible. Although facial approximation methods are useful for forensic investigation (even if they do not generate identifications through true positive recognitions of the faces), the public should beware of the marketing and political ploys employed within the profession. These ploys give rise to some impressive, but unjustifiable claims--but do not just take my word for it; evaluate the evidence for yourself with disregard to the indoctrination waged by the facial "reconstruction" field in general, including that promoted by what I have had to say here. Use your own reason and intellect and see which conclusions you reach.     

计算机辅助技术的三维颅面复原研究现状与进展

本文主要论述了国内、外学者对人头颅面三维容貌复原技术的研究现状与研究的进展情况，并对国内、外目前的头颅复原研究现状和进展情况进行了比较与分析。     

An accuracy assessment of forensic computerized facial reconstruction employing cone-beam computed tomography from live subjects

The utilization of 3D computerized systems has allowed more effective procedures for forensic facial reconstruction. Three 3D computerized facial reconstructions were produced using skull models from live adult Korean subjects to assess facial morphology prediction accuracy. The 3D skeletal and facial data were recorded from the subjects in an upright position using a cone-beam CT scanner. Shell-to-shell deviation maps were created using 3D surface comparison software, and the deviation errors between the reconstructed and target faces were measured. Results showed that 54%, 65%, and 77% of the three facial reconstruction surfaces had <2.5 mm of error when compared to the relevant target face. The average error for each reconstruction was -0.46 mm (SD = 2.81) for A, -0.31 mm (SD = 2.40) for B, and -0.49 mm (SD = 2.16) for C. The facial features of the reconstructions demonstrated good levels of accuracy compared to the target faces.     

On Gerasimov's plastic facial reconstruction technique: new insights to facilitate repeatability

Gerasimov's plastic facial reconstruction method is notoriously difficult to repeat from the published literature. Primarily, this is because of the method's underlying qualitative basis but other factors contribute including: misreports in the secondary literature of Gerasimov's method essence; a lack of published details concerning Gerasimov's modeling mastic; Gerasimov's deviation from his own published nose projection prediction guidelines; and continued refinement of the methods in the 15 years following their foremost publication. As Gerasimov cannot be consulted to resolve these issues, we provide solutions via one of his five former principal students. This includes clarification of Gerasimov's method and use of soft tissue depths; the constitution of his modeling mastic; methods used for nose projection prediction; and refinements made to his methods following their primary publication.     

Correlation Between Average Tissue Depth Data and Quantitative Accuracy of Forensic Craniofacial Reconstructions Measured by Geometric Surface Comparison Method

Accuracy is the most important factor supporting the reliability of forensic facial reconstruction (FFR) comparing to the corresponding actual face. A number of methods have been employed to evaluate objective accuracy of FFR. Recently, it has been attempted that the degree of resemblance between computer‐generated FFR and actual face is measured by geometric surface comparison method. In this study, three FFRs were produced employing live adult Korean subjects and three‐dimensional computerized modeling software. The deviations of the facial surfaces between the FFR and the head scan CT of the corresponding subject were analyzed in reverse modeling software. The results were compared with those from a previous study which applied the same methodology as this study except average facial soft tissue depth dataset. Three FFRs of this study that applied updated dataset demonstrated lesser deviation errors between the facial surfaces of the FFR and corresponding subject than those from the previous study. The results proposed that appropriate average tissue depth data are important to increase quantitative accuracy of FFR.     

Three-dimensional cranio-facial reconstruction in forensic identification: latest progress and new tendencies in the 21st century

Three-dimensional (3D) cranio-facial reconstruction can be useful in the identification of an unknown body. The progress in computer science and the improvement of medical imaging technologies during recent years had significant repercussions on this domain. New facial soft tissue depth data for children and adults have been obtained using ultrasound, CT-scans and radiographies. New guidelines for facial feature properties such as nose projection, eye protrusion or mouth width, have been suggested, but also older theories and "rules of thumbs" have been critically evaluated based on digital technology. New fast, flexible and objective 3D reconstruction computer programs are in full development. The research on craniofacial reconstruction since the beginning of the 21st century is presented, highlighting computer-aided 3D facial reconstruction. Employing the newer technologies and permanently evaluating and (re)questioning the obtained results will hopefully lead to more accurate reconstructions.     

Forensic Facial Reconstruction: A Systematic Review of Nasal Prediction Techniques

Prediction facial features from the skull are recurring theme on the forensic literature. The nose has been investigated since it has a significant role in facial recognition and the literature shows that there is not an universal method for nasal prediction. This study aimed to conduct a systematic review to analyze what are the most consistent techniques of nasal reconstruction. Primary studies describing or validating nasal reconstruction techniques in adults and published in journals of Forensic Sciences were included in this review. Tedeschi‐Oliveira's method positioned nasal tip with acceptable accuracy as Gerasimov's method. The Krogman's and Iscan's technique underestimated nasal projection in North Americans. The Prokopec's and Ubelaker's method obtained accuracy in Caucasoid. George's method presented results similar to reference standard in Caucasoid. In a supplementary way, studies are needed to accurately estimate the nasal width.     

人脸识别技术中的个人信息保护——兼论动态同意模式的建构

人脸识别技术在现有应用场景中已经被证明了其便捷性和高效性,但是,围绕该技术自身准确性、安全性、伦理性和合法性等问题的争议也从未停息。2020年我国"人脸识别第一案"引发公众及学界关注与讨论,急需在快速扩散的技术应用中,寻找信息法益保护模式。完善人脸识别技术中个人信息保护模式的可能性如下:第一,坚持"合法、正当、必要"原...     

Measuring the accuracy of facial approximations: a comparative study of resemblance rating and face array methods

The success of facial approximation is thought to depend, at least in part, upon the "accuracy" of the constructed face. However, methods of accuracy assessment are varied and this range in methods may be responsible for the disparate results reported in the literature. The aim of this study was to determine if the accuracy results of one facial approximation were comparable across two different assessment methods (resemblance ratings and simultaneous face array tests using unfamiliar assessors) and if resemblance ratings co-varied with recognition responses. True-positive recognition performance from the facial approximation was poor (21%) while resemblance scores using the same facial approximation were moderately high (3 out of 5 on a five-point scale). These results are not, therefore, consistent and indicate that either different variables are being evaluated by the methods, or the same variable is being examined but with different weight/calibration. Further resemblance ratings tests of the facial approximation to three foil faces from the face array revealed that resemblance scores were similar irrespective of which face was compared, and did not closely correspond with the degree of recognition performance. This was especially the case for isolated comparisons of single faces to the facial approximation. Collectively, these results indicate that resemblance ratings are: (i) insensitive measures of a facial approximation's accuracy; and (ii) inconsistent with results of unfamiliar simultaneous face-array recognition results. These data suggest that familiar and unfamiliar recognition tests should be given increased weight in contrast to current resemblance rating tests.