用注意力引导最优传输匹配遗骸与素描，提升法医人脸识别准确率
SPOT-Face: Forensic Face Identification using Attention Guided Optimal Transport
- 基于超像素图构建骨架与素描的结构表示，融合GNN提取特征
- 在两个公开数据集上实现召回率和mAP显著提升，优于现有图模型
- 适用于法医领域中颅骨与手绘肖像的跨模态身份比对
法医调查中若缺乏常规的DNA来源（如毛发、软组织），个人识别将变得极为困难。现有方法依赖深度学习进行人脸识别，但难以有效建模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，用于通过骨骼图像与素描图像识别受害者人脸。该统一框架首先从图像构建超像素图，再利用不同的图神经网络（GNN）骨干网络提取图嵌入，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上进行了评估。大量实验表明，所提框架在识别指标（如召回率、mAP）上显著优于现有的基于图的基线方法。此外，该框架在匹配颅骨与素描到真实人脸方面表现出极高的有效性。
原文摘要 · Abstract (English)
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.
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