可扩展的空间点过程模型用于犯罪现场鞋印分析
Scalable spatial point process models for forensic footwear analysis
AI总结：
本文提出一种可扩展的空间点过程模型，通过整合嵌套拉普拉斯近似高效处理大规模鞋印数据，利用空间变化系数提高鞋印特征识别的准确性和可靠性。
摘要：
犯罪现场恢复的鞋印证据在刑侦调查中起关键作用。通过分析鞋印，调查人员可以确定嫌疑人所穿鞋的细节。然而，确定嫌疑人鞋的型号和品牌可能不足以证明。通常，同型号、尺寸的鞋子数量庞大，任何一款都可能留下鞋印。因此，调查人员通常检查鞋印上的“意外”特征，如割伤、刮伤等，这些特征在购买后由于磨损而积累。虽然某些意外特征在某些鞋型上常见，但其他特征则高度独特，可能区分嫌疑人的鞋与其他鞋。因此，量化特征罕见性对于准确测量证据强度至关重要。在本研究中，我们通过开发一种分层贝叶斯模型来解决此任务。我们对现有方法的改进主要来自两个方面的进步。首先，我们将方法框架建模为潜在高斯模型，从而能够通过整合嵌套拉普拉斯近似高效扩展到大量标注鞋印数据。其次，我们引入空间变化系数来建模鞋底图案与意外位置之间的关系。我们通过在验证数据上表现优异来展示这些改进，从而提高犯罪鞋印分析的准确性和可靠性。
英文摘要：
Shoe print evidence recovered from crime scenes plays a key role in forensic investigations. By examining shoe prints, investigators can determine details of the footwear worn by suspects. However, establishing that a suspect's shoes match the make and model of a crime scene print may not be sufficient. Typically, thousands of shoes of the same size, make, and model are manufactured, any of which could be responsible for the print. Accordingly, a popular approach used by investigators is to examine the print for signs of ``accidentals,'' i.e., cuts, scrapes, and other features that accumulate on shoe soles after purchase due to wear. While some patterns of accidentals are common on certain types of shoes, others are highly distinctive, potentially distinguishing the suspect's shoe from all others. Quantifying the rarity of a pattern is thus essential to accurately measuring the strength of forensic evidence. In this study, we address this task by developing a hierarchical Bayesian model. Our improvement over existing methods primarily stems from two advancements. First, we frame our approach in terms of a latent Gaussian model, thus enabling inference to be efficiently scaled to large collections of annotated shoe prints via integrated nested Laplace approximations. Second, we incorporate spatially varying coefficients to model the relationship between shoes' tread patterns and accidental locations. We demonstrate these improvements through superior performance on held-out data, which enhances accuracy and reliability in forensic shoe print analysis.
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