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.

翻译：在法医调查中，当常见的DNA识别手段（如发丝、软组织）无法获取时，人员身份识别变得极具挑战。当前方法利用深度学习技术进行人脸识别。然而，这些方法缺乏有效的机制来建模两种不同法医模态间的跨域结构对应关系。本文提出SPOT-Face，一种基于超像素图的框架，专为利用骨骼图像和素描图像进行跨域司法人脸身份识别而设计。我们的统一框架包括从图像构建基于超像素的图，然后使用不同的图神经网络（GNNs）骨干网络提取这些图的嵌入表示，同时通过注意力引导的最优传输机制建立跨域对应关系。我们在两个公开数据集：IIT\_Mandi\_S2F（S2F）和CUFS上评估了所提出的框架。进行了大量实验以评估我们的框架。实验结果表明，在识别指标（即召回率、mAP）上相比现有的基于图的基线方法有显著提升。此外，我们的框架在法医调查中匹配颅骨和素描到人脸方面被证明非常有效。