# Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns

arXiv: https://arxiv.org/abs/1508.00537 · HTML: https://mlsanity.com/paper/1508.00537/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2015.

## Built on by (most cited first, 4 of 4)

- [Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising](https://mlsanity.com/paper/2111.07620/) (2021)
- [GAN pretraining for deep convolutional autoencoders applied to Software-based Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2105.10213/) (2021)
- [Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey](https://mlsanity.com/paper/2305.17522/) (2023)
- [DyFFPAD: Dynamic Fusion of Convolutional and Handcrafted Features for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2308.10015/) (2023)

## Abstract (arXiv)

With the growing use of biometric authentication systems in the past years, spoof fingerprint detection has become increasingly important. In this work, we implement and evaluate two different feature extraction techniques for software-based fingerprint liveness detection: Convolutional Networks with random weights and Local Binary Patterns. Both techniques were used in conjunction with a Support Vector Machine (SVM) classifier. Dataset Augmentation was used to increase classifier's performance and a variety of preprocessing operations were tested, such as frequency filtering, contrast equalization, and region of interest filtering. The experiments were made on the datasets used in The Liveness Detection Competition of years 2009, 2011 and 2013, which comprise almost 50,000 real and fake fingerprints' images. Our best method achieves an overall rate of 95.2% of correctly classified samples - an improvement of 35% in test error when compared with the best previously published results.