使用CNN迁移学习和可解释图像统计进行鞋底印痕性别估计
Sex Estimation from Footwear Outsole Impressions Using CNN Transfer Learning and Interpretable Image Statistics
- Fudan University（复旦大学）
- University of California, Irvine（加利福尼亚大学尔湾分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究比较CNN迁移学习与传统特征分类在鞋底印痕性别估计中的表现，发现微调CNN性能最优，并揭示其与可解释图像统计的关联，为法医筛查提供新途径。
AI中文摘要：
鞋底印痕是法医模式证据的一种常见形式，然而从这些图像中估计穿着者属性的定量方法仍相对不发达。我们通过比较卷积神经网络（CNN）迁移学习与传统的基于特征的分类，研究从鞋底印痕中进行二元性别估计。使用公开可用的鞋底印痕数据集，我们采用鞋级训练和测试划分，将同一物理鞋的重复扫描放在一起以减少数据泄漏。我们通过端到端微调、冻结特征提取后接支持向量机分类，以及结合手工设计、几何和元数据派生描述符的混合特征融合来评估预训练CNN。微调后的CNN实现了最强的整体预测性能，并且大幅优于仅使用手工指定描述符训练的传统分类器，而冻结特征方法提供了一种计算需求较低的替代方案。对低维CNN表示的探索性分析揭示了与频率阈值比、图像对比度和基于小波的摘要的关联，为学习表示与鞋底印痕可测量属性之间提供了联系。这些发现表明，CNN迁移学习捕获了超出所考虑描述符的判别信息，并为基于鞋类的法医筛查提供了一种有前景的方法。在操作使用之前，需要对独立收集的和类似案件工作的印痕进行进一步验证。
英文摘要：
Footwear outsole impressions are a common form of forensic pattern evidence, yet quantitative methods for estimating wearer attributes from these images remain relatively underdeveloped. We investigate binary sex estimation from footwear outsole impressions by comparing convolutional neural network (CNN) transfer learning with traditional feature-based classification. Using a publicly available outsole-impression dataset, we adopt a shoe-level training and test partition that keeps replicate scans of the same physical shoe together to reduce data leakage. We evaluate pretrained CNNs through end-to-end fine-tuning, frozen feature extraction followed by support vector machine classification, and hybrid feature fusion incorporating handcrafted, geometric, and metadata-derived descriptors. Fine-tuned CNNs achieve the strongest overall predictive performance and substantially outperform traditional classifiers trained on the manually specified descriptors alone, while frozen-feature approaches offer a less computationally demanding alternative. Exploratory analysis of low-dimensional CNN representations reveals associations with frequency threshold ratio, image contrast, and wavelet-based summaries, providing a connection between learned representations and measurable properties of outsole impressions. These findings suggest that CNN transfer learning captures discriminative information beyond the descriptors considered and offers a promising approach to footwear-based forensic screening. Further validation on independently collected and casework-like impressions is needed before operational use.
