Fingerprint Liveness — General Overview, Practical Application and Vulnerabilities

From Antispoofing Wiki

Fingerprint liveness is a set of countermeasures, which allow protecting fingerprint verification systems from spoofing attacks.

Definition & Problem Overview

Fingerprint liveness is a security technique, which serves to deny system access to Presentation Attacks (PAs) performed with a fingerprint replica of a legitimate user. Fingerprint recognition technology was initially developed in 1975 at NIST, where scanners were used to successfully extract fingerprint points. However, the first studies on fingerprint liveness spoofing emerged only in the early 2000s when a Japanese researcher Tsutomu Matsumoto used clear gelatin to simulate a presentation attack against a fingerprint scanner. The attack was successful at fooling 11 devices.

Fingerprint verification became a mainstream phenomenon with the advent of the smart gadgets. Even though some phones have been equipped with fingerprint sensors since 2004, the first smartphone to feature such a sensor was Motorola Atrix released in 2011. The iPhone 5 was introduced two years later featuring the first Touch ID. This prompted another research, which confirmed that fooling a phone fingerprint sensor is considerably easier as most phone scanners capture fingerprints partially. Therefore, potential for spoofing or false acceptance access grew dramatically. Another cause for concern comes from the fact that counterfeit fingerprints can be crafted from various materials. These materials are both widely available and specialized such as dental impression substances, gelatin, silicon, play-doh and even gummy bear candy.

Types of Fingerprint Presentation Attacks

Fingerprint presentation attacks can be initiated in a number of ways based on the use of presentation attack instrument (real, fake, fabricated fingerprints) to method of attack (coercion, online theft, hacking etc.).

As reported, there are three primary types of fingerprint scanning:

  • Optical. This is the simplest scanning method. It merely captures the image of a fingerprint, and is therefore, quite vulnerable to the PAs.
  • Capacitance. This method uses capacitors integrated into smartphones and offers more accurate and reliable antispoofing. However, the capacitors used can malfunction due to electrostatic discharge from user fingertips.
  • Ultrasound. Scanning is possible with ultrasound, which captures the friction ridge of a fingerprint. This type of scanning is theoretically the least vulnerable to PAs.

After scanning, the most important step in fingerprint detection is matching it to an existing database using various algorithms. The most popular print-matching algorithm is minutia matching. It analyzes information about the accessor’s finger ridges: where they end, where they split into two (bifurcation), etc.

Presentation Attacks aim to bypass these methods with the help of various techniques and tools. Experts highlight the following attack types:

  • Severed finger scenario. An exotic case, in which a deceased person’s finger can be cut off and used for authentication. However, many researchers discard this method as ineffective. Practice shows that a finger devoid of vitality signs fails to be identified, as it produces no electricity conductance.
  • Spoofing attack. A more common type, which involves a vast repertoire of Presentation Attack Instruments (PAIs). Among them are fake fingerprints fabricated using silicone, gelatin, soft rubber EcoFlex, latex, play-doh, matte paper, wood glue, latex body paint, as well as elastomeric impression materials used in dentistry.

Theft methods also vary. A target can leave their fingerprint through cooperation, while being intoxicated, threatened or unaware. Other methods allow stealing them remotely from leaked databases: 1.1 million fingerprints were stolen in 2021 as part of a hacking attack series. Another method is fingerprint jacking where a user downloads a malware app masqueraded as a benign application and is tricked to leave their fingerprint on it through its content.

A peculiar example of a remote fingerprint theft occurred in 2015, when a Chaos Communication Congress member demonstrated fingerprints of Germany’s defense minister Ursula von der Leyen, which were replicated from a few high-definition photos.

Attack Detection Methods

Two main Presentation Attack Detection (PAD) approaches are proposed. They include hardware and software approaches.

Hardware Methods

Hardware methods rely on extra gear, which allows monitoring of heart-rate oximetry, perspiration, temperature, odor, electrical parameters of the fingertips, etc. While capable of detecting vitality signs, the approach also comes with higher costs, mobility limitations, as well as challenges such as environmental dependence including external weather conditions that can affect skin temperature, etc.

Software Methods

Software based approach focuses on feature extraction and analysis that is achieved with dynamic, static or combined programming, machine learning, neural networks, etc. Commonly applied techniques include wavelet decomposition, fractional Fourier transform deviation measuring, fingertip morphology/smoothness analysis, and others.

Using Neural Networks in Fingerprint Liveness

Presentation Attack Detection in fingerprint liveness relies on Convolutional Neural Networks (CNNs) that can serve as feature extractors or classificators. Some proposed methods of using CNNs are:


CNN-VGG employs an optimized CNN with an SVM used for classification. Local Binary Patterns (LBP) are used as a baseline method, while also functioning as an illumination invariant descriptor. It achieves high accuracy — 2.9% ACE — thanks to pretraining on natural images and subsequent tuning with fingerprint images. CNN-VGG proves to be superior when compared to CNN-Alexnet and CNN-Random models as the former was trained on three Liveness Detection Competition datasets (LivDet).

Deep-Belief Network (DBN)

This approach involves a multilayered Boltzmann machine, which is trained on a dataset with bona fide, synthesized and augmented data. The LivDet 2013 test showed a 97.10% accuracy.

CNN triplet

This solution relies on the triplet objective function variant, which analyzes fingerprint pictures and differentiates fake and real fingerprints by detecting the distance between feature points.

CNN & minutia analysis

For training the network, aligned and centered local patches with 96 x 96 pixels were applied. Each training sample would receive either 1 or 0 score, where 1 means that the fingerprint is fake. (Softmax layer was used for this evaluation). Moreover, it features Fingerprint Spoof Buster — a GUI, which enables a human observer to do their own additional examining.


Fire Module of SqueezeNet is a cost-efficient solution, which requires only 2 MB of memory. The solution is based on the optimal threshold that allows to reduce misdetections. Tested with three LivDet datasets it demonstrated a 1.35% ACE rate with a 48 x 48 pixel patch size.

Other CNN-based solutions are offered as well: lightweight residual Slim-ResCNN, squared error layer CNN trained with fingerprints directly, CaffeNet + GoogleNet + Siamese trifecta, and so on.

Fingerprint Anti-spoofing Databases

There are a number of fingerprint datasets, some of which are:

LivDet database

A series of LivDet datasets was released from 2009 to 2021. For the LivDet 2021, sample material was collected from Green Bit DactyScan84C and DermalogLF10 scanners. The dataset contains 11,770 images obtained from various people.

NIST database

NIST offers three Special Databases 300-302, two of which are released as part of the N2N challenge. For example, SD 302 contains about 95 GB of various data: latent distal phalanx images, palm and fingerprint images segmented from upper palms, etc.


FVC has four datasets DB1-DB4, samples of which were either obtained with optical and thermal sweeping sensors or completely synthesized.

Fingerprint Liveness Detection Competition

A number of challenges dedicated to fingerprint liveness detection have been organized in recent years.

N2N Challenge

Nail to Nail (N2N) (2018-19) was an unassisted rolled fingerprint capture device challenge program hosted by NIST and supported by the US government together with IARPA. The goal of the contest was to develop a rolled capture device solution that would not involve an operator, while providing high-quality fingerprint images.


LivDet is an ongoing contest hosted by the Department of Electrical and Electronic Engineering and University of Cagliari. Its goal is to find the most effective solution for detecting spoofing attacks against fingerprint verification systems. It consists of three stages, the last one being the Hidden challenge featuring "unknown sensors".


  1. One of the earliest fingerprint identification examples in history (by W.J. Hershel)
  2. Biometric Technology: A Brief History
  3. What is gummy bear hack?
  4. Fingerprint scanner on Phones: History and Evolution, but do we really need that?
  5. Motorola Atrix 4G
  6. Apple Announces iPhone 5s—The Most Forward-Thinking Smartphone in the World
  7. MasterPrint: Exploring the Vulnerability of Partial Fingerprint-Based Authentication Systems
  8. Gummy Bears can be used by hackers to make a counterfeit fingerprint to fool your scanner
  9. A comprehensive survey of fingerprint presentation attack detection
  10. Fundamentals of fingerprint scanning
  11. Fingerprint Minutiae Matching Based on the Local and Global Structures
  12. Fingerprint indexing based on minutiae pairs and convex core point
  13. Robust anti-spoofing techniques for fingerprint liveness detection: A Survey
  14. Why Dead Fingers (Usually) Can't Unlock a Phone
  15. Dental Impression Materials
  16. Massive Fingerprint Hack Illustrates Why Anonymous Biometrics Are Critical To Security
  17. 'Fingerprint-Jacking' Attack Technique Manipulates Android UI
  18. Hacker fakes German minister's fingerprints using photos of her hands
  19. Fingerprint Liveness Detection using Convolutional Neural Networks
  20. VGG architecture
  21. Fingerprint Spoof Buster: Use of Minutiae-Centered Patches
  22. Patch-based Fake Fingerprint Detection Using a Fully Convolutional Neural Network with a Small Number of Parameters and an Optimal Threshold
  23. LivDet Databases
  24. Biometric Special Databases and Software
  25. A Survey on Antispoofing Schemes for Fingerprint Recognition Systems
  26. FVC has four datasets DB1-DB4
  27. Nail to Nail (N2N) Fingerprint Capture Challenge
  28. IARPA
  29. LivDet
  30. Dipartimento di Ingegneria elettrica ed elettronica