# Learning a Fixed-Length Fingerprint Representation

arXiv: https://arxiv.org/abs/1909.09901 · HTML: https://mlsanity.com/paper/1909.09901/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2019.

## Built on by (most cited first, 30 of 49)

- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [PrintsGAN: Synthetic Fingerprint Generator](https://mlsanity.com/paper/2201.03674/) (2022)
- [C2CL: Contact to Contactless Fingerprint Matching](https://mlsanity.com/paper/2104.02811/) (2021)
- [Synthetic Data in Human Analysis: A Survey](https://mlsanity.com/paper/2208.09191/) (2022)
- [AFR-Net: Attention-Driven Fingerprint Recognition Network](https://mlsanity.com/paper/2211.13897/) (2022)
- [SpoofGAN: Synthetic Fingerprint Spoof Images](https://mlsanity.com/paper/2204.06498/) (2022)
- [IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer](https://mlsanity.com/paper/2404.08237/) (2024)
- [Latent Fingerprint Recognition: Fusion of Local and Global Embeddings](https://mlsanity.com/paper/2304.13800/) (2023)
- [Monocular 3D Fingerprint Reconstruction and Unwarping](https://mlsanity.com/paper/2205.00967/) (2022)
- [Synthetic Latent Fingerprint Generator](https://mlsanity.com/paper/2208.13811/) (2022)
- [Benchmarking fixed-length Fingerprint Representations across different Embedding Sizes and Sensor Types](https://mlsanity.com/paper/2307.08615/) (2023)
- [Fixed-length Dense Descriptor for Efficient Fingerprint Matching](https://mlsanity.com/paper/2311.18576/) (2023)
- [FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data](https://mlsanity.com/paper/2310.19024/) (2023)
- [Phase-aggregated Dual-branch Network for Efficient Fingerprint Dense Registration](https://mlsanity.com/paper/2404.17159/) (2024)
- [Regression of Dense Distortion Field from a Single Fingerprint Image](https://mlsanity.com/paper/2404.17610/) (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)
- [Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learning](https://mlsanity.com/paper/2101.05260/) (2021)
- [HEFT: Homomorphically Encrypted Fusion of Biometric Templates](https://mlsanity.com/paper/2208.07241/) (2022)
- [Joint Person Identity, Gender and Age Estimation from Hand Images using Deep Multi-Task Representation Learning](https://mlsanity.com/paper/2303.15263/) (2023)
- [IDFace: Face Template Protection for Efficient and Secure Identification](https://mlsanity.com/paper/2507.12050/) (2025)
- [Child Palm-ID: Contactless Palmprint Recognition for Children](https://mlsanity.com/paper/2305.05161/) (2023)
- [MRA-GNN: Minutiae Relation-Aware Model over Graph Neural Network for Fingerprint Embedding](https://mlsanity.com/paper/2307.16416/) (2023)
- [Privacy-preserving Multi-biometric Indexing based on Frequent Binary Patterns](https://mlsanity.com/paper/2310.03091/) (2023)
- [Blind-Match: Efficient Homomorphic Encryption-Based 1:N Matching for Privacy-Preserving Biometric Identification](https://mlsanity.com/paper/2408.06167/) (2024)
- [Securing Face and Fingerprint Templates in Humanitarian Biometric Systems](https://mlsanity.com/paper/2508.18415/) (2025)
- [Fingerprint Template Invertibility: Minutiae vs. Deep Templates](https://mlsanity.com/paper/2205.03809/) (2022)
- [Learning an Ensemble of Deep Fingerprint Representations](https://mlsanity.com/paper/2209.02425/) (2022)
- [Multi-Biometric Fuzzy Vault based on Face and Fingerprints](https://mlsanity.com/paper/2301.06882/) (2023)

## Abstract (arXiv)

We present DeepPrint, a deep network, which learns to extract fixed-length fingerprint representations of only 200 bytes. DeepPrint incorporates fingerprint domain knowledge, including alignment and minutiae detection, into the deep network architecture to maximize the discriminative power of its representation. The compact, DeepPrint representation has several advantages over the prevailing variable length minutiae representation which (i) requires computationally expensive graph matching techniques, (ii) is difficult to secure using strong encryption schemes (e.g. homomorphic encryption), and (iii) has low discriminative power in poor quality fingerprints where minutiae extraction is unreliable. We benchmark DeepPrint against two top performing COTS SDKs (Verifinger and Innovatrics) from the NIST and FVC evaluations. Coupled with a re-ranking scheme, the DeepPrint rank-1 search accuracy on the NIST SD4 dataset against a gallery of 1.1 million fingerprints is comparable to the top COTS matcher, but it is significantly faster (DeepPrint: 98.80% in 0.3 seconds vs. COTS A: 98.85% in 27 seconds). To the best of our knowledge, the DeepPrint representation is the most compact and discriminative fixed-length fingerprint representation reported in the academic literature.