# A Latent Fingerprint in the Wild Database

arXiv: https://arxiv.org/abs/2304.00979 · HTML: https://mlsanity.com/paper/2304.00979/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 59 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 8 of 8)

- [On the Intrinsic Dimensionality of Image Representations](https://mlsanity.com/paper/1803.09672/) (2018)
- [Automated Latent Fingerprint Recognition](https://mlsanity.com/paper/1704.01925/) (2017)
- [Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge](https://mlsanity.com/paper/1712.09401/) (2017)
- [FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement](https://mlsanity.com/paper/2206.12885/) (2022)
- [Latent fingerprint minutia extraction using fully convolutional network](https://mlsanity.com/paper/1609.09850/) (2016)
- [Latent Fingerprint Recognition: Role of Texture Template](https://mlsanity.com/paper/1804.10337/) (2018)
- [Automatic Latent Fingerprint Segmentation](https://mlsanity.com/paper/1804.09650/) (2018)
- [End-to-End Pore Extraction and Matching in Latent Fingerprints: Going Beyond Minutiae](https://mlsanity.com/paper/1905.11472/) (2019)

## Built on by (most cited first, 7 of 7)

- [Universal Fingerprint Generation: Controllable Diffusion Model with Multimodal Conditions](https://mlsanity.com/paper/2404.13791/) (2024)
- [A Comprehensive Re-Evaluation of Biometric Modality Properties in the Modern Era](https://mlsanity.com/paper/2508.13874/) (2025)
- [LatentPrintFormer: A Hybrid CNN-Transformer with Spatial Attention for Latent Fingerprint identification](https://mlsanity.com/paper/2511.08119/) (2025)
- [Continual Few-shot Adaptation for Synthetic Fingerprint Detection](https://mlsanity.com/paper/2603.14632/) (2026)
- [Intra-finger Variability of Diffusion-based Latent Fingerprint Generation](https://mlsanity.com/paper/2604.10040/) (2026)
- [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)

Latent fingerprints are among the most important and widely used evidence in crime scenes, digital forensics and law enforcement worldwide. Despite the number of advancements reported in recent works, we note that significant open issues such as independent benchmarking and lack of large-scale evaluation databases for improving the algorithms are inadequately addressed. The available databases are mostly of semi-public nature, lack of acquisition in the wild environment, and post-processing pipelines. Moreover, they do not represent a realistic capture scenario similar to real crime scenes, to benchmark the robustness of the algorithms. Further, existing databases for latent fingerprint recognition do not have a large number of unique subjects/fingerprint instances or do not provide ground truth/reference fingerprint images to conduct a cross-comparison against the latent. In this paper, we introduce a new wild large-scale latent fingerprint database that includes five different acquisition scenarios: reference fingerprints from (1) optical and (2) capacitive sensors, (3) smartphone fingerprints, latent fingerprints captured from (4) wall surface, (5) Ipad surface, and (6) aluminium foil surface. The new database consists of 1,318 unique fingerprint instances captured in all above mentioned settings. A total of 2,636 reference fingerprints from optical and capacitive sensors, 1,318 fingerphotos from smartphones, and 9,224 latent fingerprints from each of the 132 subjects were provided in this work. The dataset is constructed considering various age groups, equal representations of genders and backgrounds. In addition, we provide an extensive set of analysis of various subset evaluations to highlight open challenges for future directions in latent fingerprint recognition research.