Abstract
Person identification in forensic investigations becomes very challenging when common identification means for DNA (i.e., hair strands, soft tissue) are not available. Current methods utilize deep learning methods for face recognition. However, these methods lack effective mechanisms to model cross-domain structural correspondence between two different forensic modalities. In this paper, we introduce a SPOT-Face, a superpixel graph-based framework designed for cross-domain forensic face identification of victims using their skeleton and sketch images. Our unified framework involves constructing a superpixel-based graph from an image and then using different graph neural networks(GNNs) backbones to extract the embeddings of these graphs, while cross-domain correspondence is established through attention-guided optimal transport mechanism. We have evaluated our proposed framework on two publicly available dataset: IIT\_Mandi\_S2F (S2F) and CUFS. Extensive experiments were conducted to evaluate our proposed framework. The experimental results show significant improvement in identification metrics ( i.e., Recall, mAP) over existing graph-based baselines. Furthermore, our framework demonstrates to be highly effective for matching skulls and sketches to faces in forensic investigations.
Abstract (translated)
在法医调查中，当常见的DNA鉴定手段（如毛发、软组织）不可用时，人员识别变得非常具有挑战性。目前的方法主要依赖于深度学习技术进行人脸识别，但这些方法缺乏有效的机制来建模两种不同法医模式之间的跨域结构对应关系。本文介绍了一种名为SPOT-Face的框架，该框架基于超像素图，旨在使用受害者的骨骼和素描图像来进行跨域法医面部识别。 我们的统一框架包括从一张图片中构建一个基于超像素的图，并利用不同的图神经网络（GNN）骨干来提取这些图的嵌入，同时通过注意力引导的最佳传输机制建立跨域对应关系。我们在两个公开的数据集上评估了我们提出的框架：IIT_Mandi_S2F (S2F) 和 CUFS。 进行了广泛的实验以评估我们的提议框架。实验证明，在识别指标（如召回率、mAP）方面，与现有的基于图的基线相比，有了显著改进。此外，我们的框架还表现出强大的能力，能够将颅骨和素描匹配到面部图像中，这对于法医调查来说非常有效。 该研究提供了一种创新的方法来解决跨模态法医识别中的挑战，特别是在缺乏DNA样本的情况下，为受害者身份的确定提供了新的可能性。
URL
https://arxiv.org/abs/2601.09229
