# EXPRESSNET: An Explainable Residual Slim Network for Fingerprint Presentation Attack Detection

arXiv: https://arxiv.org/abs/2305.09397 · HTML: https://mlsanity.com/paper/2305.09397/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models](https://mlsanity.com/paper/1708.08296/) (2017)
- [Explainable Deep Learning: A Field Guide for the Uninitiated](https://mlsanity.com/paper/2004.14545/) (2020)
- [Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges](https://mlsanity.com/paper/1803.07517/) (2018)
- [Fingerprint Spoof Buster](https://mlsanity.com/paper/1712.04489/) (2017)
- [Analyzing Classifiers: Fisher Vectors and Deep Neural Networks](https://mlsanity.com/paper/1512.00172/) (2015)
- [Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AI](https://mlsanity.com/paper/2007.06712/) (2020)
- [LivDet in Action - Fingerprint Liveness Detection Competition 2019](https://mlsanity.com/paper/1905.00639/) (2019)
- [Fingerprint Presentation Attack Detection Based on Local Features Encoding for Unknown Attacks](https://mlsanity.com/paper/1908.10163/) (2019)
- [Multi-Modal Fingerprint Presentation Attack Detection: Evaluation On A New Dataset](https://mlsanity.com/paper/2006.07498/) (2020)
- [End-to-End Fingerprints Liveness Detection using Convolutional Networks with Gram module](https://mlsanity.com/paper/1803.07830/) (2018)
- [Fingerprint liveness detection using local quality features](https://mlsanity.com/paper/1806.02974/) (2018)
- [DeFraudNet:End2End Fingerprint Spoof Detection using Patch Level Attention](https://mlsanity.com/paper/2002.08214/) (2020)

## Abstract (arXiv)

Presentation attack is a challenging issue that persists in the security of automatic fingerprint recognition systems. This paper proposes a novel explainable residual slim network that detects the presentation attack by representing the visual features in the input fingerprint sample. The encoder-decoder of this network along with the channel attention block converts the input sample into its heatmap representation while the modified residual convolutional neural network classifier discriminates between live and spoof fingerprints. The entire architecture of the heatmap generator block and modified ResNet classifier works together in an end-to-end manner. The performance of the proposed model is validated on benchmark liveness detection competition databases i.e. Livdet 2011, 2013, 2015, 2017, and 2019 and the classification accuracy of 96.86%, 99.84%, 96.45%, 96.07%, 96.27% are achieved on them, respectively. The performance of the proposed model is compared with the state-of-the-art techniques, and the proposed method outperforms state-of-the-art methods in benchmark protocols of presentation attack detection in terms of classification accuracy.