# Finger-NestNet: Interpretable Fingerphoto Verification on Smartphone using Deep Nested Residual Network

arXiv: https://arxiv.org/abs/2212.05884 · HTML: https://mlsanity.com/paper/2212.05884/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 9 Dec 2022.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Why ResNet Works? Residuals Generalize](https://mlsanity.com/paper/1904.01367/) (2019)
- [Taming the Cross Entropy Loss](https://mlsanity.com/paper/1810.05075/) (2018)
- [Mobile Touchless Fingerprint Recognition: Implementation, Performance and Usability Aspects](https://mlsanity.com/paper/2103.03038/) (2021)
- [Periocular Biometrics and its Relevance to Partially Masked Faces: A Survey](https://mlsanity.com/paper/2203.15203/) (2022)
- [Matching Fingerphotos to Slap Fingerprint Images](https://mlsanity.com/paper/1804.08122/) (2018)
- [Smartphone Multi-modal Biometric Authentication: Database and Evaluation](https://mlsanity.com/paper/1912.02487/) (2019)
- [A Contactless Fingerprint Recognition System](https://mlsanity.com/paper/2108.09048/) (2021)

## Built on by (most cited first, 3 of 3)

- [Deep Features for Contactless Fingerprint Presentation Attack Detection: Can They Be Generalized?](https://mlsanity.com/paper/2307.01845/) (2023)
- [Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey](https://mlsanity.com/paper/2305.17522/) (2023)
- [Recent Advancement in 3D Biometrics using Monocular Camera](https://mlsanity.com/paper/2401.02646/) (2024)

## Abstract (arXiv)

Fingerphoto images captured using a smartphone are successfully used to verify the individuals that have enabled several applications. This work presents a novel algorithm for fingerphoto verification using a nested residual block: Finger-NestNet. The proposed Finger-NestNet architecture is designed with three consecutive convolution blocks followed by a series of nested residual blocks to achieve reliable fingerphoto verification. This paper also presents the interpretability of the proposed method using four different visualization techniques that can shed light on the critical regions in the fingerphoto biometrics that can contribute to the reliable verification performance of the proposed method. Extensive experiments are performed on the fingerphoto dataset comprised of 196 unique fingers collected from 52 unique data subjects using an iPhone6S. Experimental results indicate the improved verification of the proposed method compared to six different existing methods with EER = 1.15%.