Deducing the 3D face from a skull is an essential but challenging task in
forensic science and archaeology. Existing methods for automated facial
reconstruction yield inaccurate results, suffering from the non-determinative
nature of the problem that a skull with a sparse set of tissue depth cannot
fully determine the skinned face. Additionally, their texture-less results
require further post-processing stages to achieve a photo-realistic appearance.
This paper proposes an end-to-end 3D face reconstruction and exploration tool,
providing textured 3D faces for reference. With the help of state-of-the-art
text-to-image diffusion models and image-based facial reconstruction
techniques, we generate an initial reference 3D face, whose biological profile
aligns with the given skull. We then adapt these initial faces to meet the
statistical expectations of extruded anatomical landmarks on the skull through
an optimization process. The joint statistical distribution of tissue depths is
learned on a small set of anatomical landmarks on the skull. To support further
adjustment, we propose an efficient face adaptation tool to assist users in
tuning tissue depths, either globally or at local regions, while observing
plausible visual feedback. Experiments conducted on a real skull-face dataset
demonstrated the effectiveness of our proposed pipeline in terms of
reconstruction accuracy, diversity, and stability.

提出了一种端到端的 3D 面部重建和探索工具，使用最新的文本到图像扩散模型和基于图像的面部重建技术生成初始参考 3D 面部，然后通过优化过程将这些初始面部调整为与给定头骨上的解剖标志的统计预期相符，通过小型解剖标志上的组合统计分布学习组织厚度，同时提出了一种高效的面部调整工具来辅助用户在观察可行的视觉反馈的同时全局或局部地调整组织厚度。实验证明了所提出流程在重建精度、多样性和稳定性方面的有效性。

头骨到面部：解剖导向的 3D 面部重建与编辑

Skull-to-Face: Anatomy-Guided 3D Facial Reconstruction and Editing

XAI (eXplanable AI) techniques that have the property of explaining the
reasons for their conclusions, i.e. explainability or interpretability, are
attracting attention. XAI is expected to be used in the development of forensic
science and the justice system. In today's forensic and criminal investigation
environment, experts face many challenges due to large amounts of data, small
pieces of evidence in a chaotic and complex environment, traditional laboratory
structures and sometimes inadequate knowledge. All these can lead to failed
investigations and miscarriages of justice. In this paper, we describe the
application of one logical approach to crime scene investigation. The subject
of the application is ``The Adventure of the Speckled Band'' from the Sherlock
Holmes short stories. The applied data is the knowledge graph created for the
Knowledge Graph Reasoning Challenge. We tried to find the murderer by inferring
each person with the motive, opportunity, and method. We created an ontology of
motives and methods of murder from dictionaries and dictionaries, added it to
the knowledge graph of ``The Adventure of the Speckled Band'', and applied
scripts to determine motives, opportunities, and methods.

应用 XAI 技术和逻辑推理方法，在犯罪现场调查中找到谋杀犯人的动机、机会和手段。

刑事案件调查的逻辑方法

A Logical Approach to Criminal Case Investigation

Handwritten document analysis is an area of forensic science, with the goal
of establishing authorship of documents through examination of inherent
characteristics. Law enforcement agencies use standard protocols based on
manual processing of handwritten documents. This method is time-consuming, is
often subjective in its evaluation, and is not replicable. To overcome these
limitations, in this paper we present a framework capable of extracting and
analyzing intrinsic measures of manuscript documents related to text line
heights, space between words, and character sizes using image processing and
deep learning techniques. The final feature vector for each document involved
consists of the mean and standard deviation for every type of measure
collected. By quantifying the Euclidean distance between the feature vectors of
the documents to be compared, authorship can be discerned. We also proposed a
new and challenging dataset consisting of 362 handwritten manuscripts written
on paper and digital devices by 124 different people. Our study pioneered the
comparison between traditionally handwritten documents and those produced with
digital tools (e.g., tablets). Experimental results demonstrate the ability of
our method to objectively determine authorship in different writing media,
outperforming the state of the art.

通过图像处理和深度学习技术，本文提出了一个能够提取和分析手稿文档与文本行高度、单词间距和字符大小相关的内在度量的框架。通过量化待比较文档的特征向量之间的欧几里德距离，可以辨别出作者身份。我们还提出了一个新的、具有挑战性的数据集，包括 124 个不同人物撰写的 362 个手写和数字设备上的手稿。实验结果表明，我们的方法能够在不同的写作媒介中客观地确定作者身份，并超越现有技术水平。

手写文档的非破坏性检验创新方法

Innovative Methods for Non-Destructive Inspection of Handwritten  Documents

Forensic science heavily relies on analyzing latent fingerprints, which are
crucial for criminal investigations. However, various challenges, such as
background noise, overlapping prints, and contamination, make the
identification process difficult. Moreover, limited access to real crime scene
and laboratory-generated databases hinders the development of efficient
recognition algorithms. This study aims to develop a fast method, which we call
ULPrint, to enhance various latent fingerprint types, including those obtained
from real crime scenes and laboratory-created samples, to boost fingerprint
recognition system performance. In closed-set identification accuracy
experiments, the enhanced image was able to improve the performance of the
MSU-AFIS from 61.56\% to 75.19\% in the NIST SD27 database, from 67.63\% to
77.02\% in the MSP Latent database, and from 46.90\% to 52.12\% in the NIST
SD302 database. Our contributions include (1) the development of a two-step
latent fingerprint enhancement method that combines Ridge Segmentation with
UNet and Mix Visual Transformer (MiT) SegFormer-B5 encoder architecture, (2)
the implementation of multiple dilated convolutions in the UNet architecture to
capture intricate, non-local patterns better and enhance ridge segmentation,
and (3) the guided blending of the predicted ridge mask with the latent
fingerprint. This novel approach, ULPrint, streamlines the enhancement process,
addressing challenges across diverse latent fingerprint types to improve
forensic investigations and criminal justice outcomes.

本研究旨在开发一种名为 ULPrint 的快速方法，以增强各种潜在指纹类型，包括从真实犯罪现场和实验室创建的样本中获得的指纹，从而提高指纹识别系统的性能。这种新方法通过开发一个基于 Ridge 分段、UNet 和 Mix Visual Transformer (MiT) SegFormer-B5 编码器架构的两步潜在指纹增强方法来解决不同潜在指纹类型的挑战，提高了司法调查和刑事司法结果。