# End-to-End Latent Fingerprint Search

arXiv: https://arxiv.org/abs/1812.10213 · HTML: https://mlsanity.com/paper/1812.10213/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2018.

## Built on by (most cited first, 14 of 14)

- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [C2CL: Contact to Contactless Fingerprint Matching](https://mlsanity.com/paper/2104.02811/) (2021)
- [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)
- [A Unified Model for Fingerprint Authentication and Presentation Attack Detection](https://mlsanity.com/paper/2104.03255/) (2021)
- [Fingerprint Template Invertibility: Minutiae vs. Deep Templates](https://mlsanity.com/paper/2205.03809/) (2022)
- [LEADER: Lightweight End-to-End Attention-Gated Dual Autoencoder for Robust Minutiae Extraction](https://mlsanity.com/paper/2602.15493/) (2026)
- [Unleashing the Power of Simplicity: A Minimalist Strategy for State-of-the-Art Fingerprint Enhancement](https://mlsanity.com/paper/2603.19004/) (2026)
- [Pair-Relationship Modeling for Latent Fingerprint Recognition](https://mlsanity.com/paper/2207.00587/) (2022)
- [A triple-branch network for latent fingerprint enhancement guided by orientation fields and minutiae](https://mlsanity.com/paper/2504.15105/) (2025)
- [Minutiae-Anchored Local Dense Representation for Fingerprint Matching](https://mlsanity.com/paper/2507.15297/) (2025)
- [Print2Volume: Generating Synthetic OCT-based 3D Fingerprint Volume from 2D Fingerprint Image](https://mlsanity.com/paper/2508.21371/) (2025)
- [LatentPrintFormer: A Hybrid CNN-Transformer with Spatial Attention for Latent Fingerprint identification](https://mlsanity.com/paper/2511.08119/) (2025)
- [AFID: A Unified Open Framework for Automated Fingermark Identification, Quality Assessment and Feature Extraction](https://mlsanity.com/paper/2609.07439/) (2026)

## Abstract (arXiv)

Latent fingerprints are one of the most important and widely used sources of evidence in law enforcement and forensic agencies. Yet the performance of the state-of-the-art latent recognition systems is far from satisfactory, and they often require manual markups to boost the latent search performance. Further, the COTS systems are proprietary and do not output the true comparison scores between a latent and reference prints to conduct quantitative evidential analysis. We present an end-to-end latent fingerprint search system, including automated region of interest (ROI) cropping, latent image preprocessing, feature extraction, feature comparison , and outputs a candidate list. Two separate minutiae extraction models provide complementary minutiae templates. To compensate for the small number of minutiae in small area and poor quality latents, a virtual minutiae set is generated to construct a texture template. A 96-dimensional descriptor is extracted for each minutia from its neighborhood. For computational efficiency, the descriptor length for virtual minutiae is further reduced to 16 using product quantization. Our end-to-end system is evaluated on three latent databases: NIST SD27 (258 latents); MSP (1,200 latents), WVU (449 latents) and N2N (10,000 latents) against a background set of 100K rolled prints, which includes the true rolled mates of the latents with rank-1 retrieval rates of 65.7%, 69.4%, 65.5%, and 7.6% respectively. A multi-core solution implemented on 24 cores obtains 1ms per latent to rolled comparison.