# Latent Fingerprint Recognition: Fusion of Local and Global Embeddings

arXiv: https://arxiv.org/abs/2304.13800 · HTML: https://mlsanity.com/paper/2304.13800/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 58 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [Learning a Fixed-Length Fingerprint Representation](https://mlsanity.com/paper/1909.09901/) (2019)
- [FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction](https://mlsanity.com/paper/1709.02228/) (2017)
- [PrintsGAN: Synthetic Fingerprint Generator](https://mlsanity.com/paper/2201.03674/) (2022)
- [C2CL: Contact to Contactless Fingerprint Matching](https://mlsanity.com/paper/2104.02811/) (2021)
- [AFR-Net: Attention-Driven Fingerprint Recognition Network](https://mlsanity.com/paper/2211.13897/) (2022)
- [Automated Latent Fingerprint Recognition](https://mlsanity.com/paper/1704.01925/) (2017)
- [SpoofGAN: Synthetic Fingerprint Spoof Images](https://mlsanity.com/paper/2204.06498/) (2022)
- [FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement](https://mlsanity.com/paper/2206.12885/) (2022)
- [Synthetic Latent Fingerprint Generator](https://mlsanity.com/paper/2208.13811/) (2022)
- [Matching Fingerphotos to Slap Fingerprint Images](https://mlsanity.com/paper/1804.08122/) (2018)
- [ID Preserving Generative Adversarial Network for Partial Latent Fingerprint Reconstruction](https://mlsanity.com/paper/1808.00035/) (2018)
- [Latent fingerprint minutia extraction using fully convolutional network](https://mlsanity.com/paper/1609.09850/) (2016)
- [Generative Convolutional Networks for Latent Fingerprint Reconstruction](https://mlsanity.com/paper/1705.01707/) (2017)
- [Latent Fingerprint Registration via Matching Densely Sampled Points](https://mlsanity.com/paper/2005.05878/) (2020)
- [Latent Fingerprint Recognition: Role of Texture Template](https://mlsanity.com/paper/1804.10337/) (2018)
- [White-Box Evaluation of Fingerprint Matchers: Robustness to Minutiae Perturbations](https://mlsanity.com/paper/1909.00799/) (2019)

## Built on by (most cited first, 13 of 13)

- [KeyPoint Relative Position Encoding for Face Recognition](https://mlsanity.com/paper/2403.14852/) (2024)
- [Universal Fingerprint Generation: Controllable Diffusion Model with Multimodal Conditions](https://mlsanity.com/paper/2404.13791/) (2024)
- [Fixed-length Dense Descriptor for Efficient Fingerprint Matching](https://mlsanity.com/paper/2311.18576/) (2023)
- [Joint Identity Verification and Pose Alignment for Partial Fingerprints](https://mlsanity.com/paper/2405.03959/) (2024)
- [Mobile Contactless Palmprint Recognition: Use of Multiscale, Multimodel Embeddings](https://mlsanity.com/paper/2401.08111/) (2024)
- [Latent Fingerprint Matching via Dense Minutia Descriptor](https://mlsanity.com/paper/2405.01199/) (2024)
- [Finger Pose Estimation for Under-screen Fingerprint Sensor](https://mlsanity.com/paper/2505.02481/) (2025)
- [Fixed-Length Dense Fingerprint Representation with Alignment and Robust Enhancement](https://mlsanity.com/paper/2505.03597/) (2025)
- [Child Palm-ID: Contactless Palmprint Recognition for Children](https://mlsanity.com/paper/2305.05161/) (2023)
- [Minutiae-Anchored Local Dense Representation for Fingerprint Matching](https://mlsanity.com/paper/2507.15297/) (2025)
- [LatentPrintFormer: A Hybrid CNN-Transformer with Spatial Attention for Latent Fingerprint identification](https://mlsanity.com/paper/2511.08119/) (2025)
- [Progressive Learning of a Diffusion-based Inpainting Model for Separating Overlapped Fingerprints](https://mlsanity.com/paper/2608.03937/) (2026)
- [AFID: A Unified Open Framework for Automated Fingermark Identification, Quality Assessment and Feature Extraction](https://mlsanity.com/paper/2609.07439/) (2026)

## Abstract (arXiv)

One of the most challenging problems in fingerprint recognition continues to be establishing the identity of a suspect associated with partial and smudgy fingerprints left at a crime scene (i.e., latent prints or fingermarks). Despite the success of fixed-length embeddings for rolled and slap fingerprint recognition, the features learned for latent fingerprint matching have mostly been limited to local minutiae-based embeddings and have not directly leveraged global representations for matching. In this paper, we combine global embeddings with local embeddings for state-of-the-art latent to rolled matching accuracy with high throughput. The combination of both local and global representations leads to improved recognition accuracy across NIST SD 27, NIST SD 302, MSP, MOLF DB1/DB4, and MOLF DB2/DB4 latent fingerprint datasets for both closed-set (84.11%, 54.36%, 84.35%, 70.43%, 62.86% rank-1 retrieval rate, respectively) and open-set (0.50, 0.74, 0.44, 0.60, 0.68 FNIR at FPIR=0.02, respectively) identification scenarios on a gallery of 100K rolled fingerprints. Not only do we fuse the complimentary representations, we also use the local features to guide the global representations to focus on discriminatory regions in two fingerprint images to be compared. This leads to a multi-stage matching paradigm in which subsets of the retrieved candidate lists for each probe image are passed to subsequent stages for further processing, resulting in a considerable reduction in latency (requiring just 0.068 ms per latent to rolled comparison on a AMD EPYC 7543 32-Core Processor, roughly 15K comparisons per second). Finally, we show the generalizability of the fused representations for improving authentication accuracy across several rolled, plain, and contactless fingerprint datasets.