# SPOT-Face: Forensic Face Identification using Attention Guided Optimal Transport

arXiv: https://arxiv.org/abs/2601.09229 · HTML: https://mlsanity.com/paper/2601.09229/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 14 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Cross-Batch Memory for Embedding Learning](https://mlsanity.com/paper/1912.06798/) (2019)
- [Learning Combinatorial Embedding Networks for Deep Graph Matching](https://mlsanity.com/paper/1904.00597/) (2019)
- [StyleMeUp: Towards Style-Agnostic Sketch-Based Image Retrieval](https://mlsanity.com/paper/2103.15706/) (2021)
- [MIC: Mining Interclass Characteristics for Improved Metric Learning](https://mlsanity.com/paper/1909.11574/) (2019)
- [Divide and Conquer the Embedding Space for Metric Learning](https://mlsanity.com/paper/1906.05990/) (2019)
- [Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition](https://mlsanity.com/paper/2103.16019/) (2021)
- [Cross-Domain Identity Representation for Skull to Face Matching with Benchmark DataSet](https://mlsanity.com/paper/2507.08329/) (2025)
- [FCR: Investigating Generative AI models for Forensic Craniofacial Reconstruction](https://mlsanity.com/paper/2508.18031/) (2025)

## Built on by (most cited first, 1 of 1)

- [Cranio-Diff: Diffusion-based Cross-domain Craniofacial Reconstruction with 2D X-ray Skull Guidance and Structural Identity Constraints](https://mlsanity.com/paper/2606.09699/) (2026)

## Abstract (arXiv)

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: IITMandiS2F (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.