Footwear examiners are tasked with comparing an outsole impression (Q) left at a crime scene with an impression (K) from a database or from the suspect's shoe. We propose a method for comparing two shoe outsole impressions that relies on robust features (speeded‐up robust feature; SURF) on each impression and aligns them using a maximum clique (MC). After alignment, an algorithm we denote MC‐COMP is used to extract additional features that are then combined into a univariate similarity score using a random forest (RF). We use a database of shoe outsole impressions that includes images from two models of athletic shoes that were purchased new and then worn by study participants for about 6 months. The shoes share class characteristics such as outsole pattern and size, and thus the comparison is challenging. We find that the RF implemented on SURF outperforms other methods recently proposed in the literature in terms of classification precision. In more realistic scenarios where crime scene impressions may be degraded and smudged, the algorithm we propose—denoted MC‐COMP‐SURF—shows the best classification performance by detecting unique features better than other methods. The algorithm can be implemented with the R‐package shoeprintr.

中文翻译：

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一种使用最大团和加速鲁棒特征来比较二维鞋外底图像的算法


鞋类检验员的任务是将犯罪现场留下的外底印记 ( Q ) 与数据库或嫌疑人鞋子上的印记 ( K ) 进行比较。我们提出了一种比较两个鞋外底印象的方法，该方法依赖于每个印象的鲁棒特征（加速鲁棒特征；SURF），并使用最大团（MC）将它们对齐。对齐后，使用我们表示的 MC-COMP 算法来提取附加特征，然后使用随机森林 (RF) 将这些特征组合成单变量相似度得分。我们使用鞋外底印象数据库，其中包括两种型号的运动鞋的图像，这些运动鞋是新购买的，然后被研究参与者穿着了大约 6 个月。这两款鞋具有相同的类别特征，例如外底图案和尺寸，因此比较具有挑战性。我们发现，在 SURF 上实现的 RF 在分类精度方面优于文献中最近提出的其他方法。在更现实的场景中，犯罪现场印象可能会被降低和弄脏，我们提出的算法（表示为 MC-COMP-SURF）通过比其他方法更好地检测独特特征来显示最佳分类性能。该算法可以使用 R 包Shoeprintr来实现。