# Biometrics Recognition Using Deep Learning: A Survey

arXiv: https://arxiv.org/abs/1912.00271 · HTML: https://mlsanity.com/paper/1912.00271/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2019.

## Built on by (most cited first, 11 of 11)

- [Going Deeper Into Face Detection: A Survey](https://mlsanity.com/paper/2103.14983/) (2021)
- [Eight Years of Face Recognition Research: Reproducibility, Achievements and Open Issues](https://mlsanity.com/paper/2208.04040/) (2022)
- [Masked Face Recognition using ResNet-50](https://mlsanity.com/paper/2104.08997/) (2021)
- [MISA: Online Defense of Trojaned Models using Misattributions](https://mlsanity.com/paper/2103.15918/) (2021)
- [Roadmap on Signal Processing for Next Generation Measurement Systems](https://mlsanity.com/paper/2111.02493/) (2021)
- [Joint Person Identity, Gender and Age Estimation from Hand Images using Deep Multi-Task Representation Learning](https://mlsanity.com/paper/2303.15263/) (2023)
- [Towards On-Device Face Recognition in Body-worn Cameras](https://mlsanity.com/paper/2104.03419/) (2021)
- [Automatic Speech Recognition And Limited Vocabulary: A Survey](https://mlsanity.com/paper/2108.10254/) (2021)
- [A Study of Multimodal Person Verification Using Audio-Visual-Thermal Data](https://mlsanity.com/paper/2110.12136/) (2021)
- [Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches](https://mlsanity.com/paper/2202.10320/) (2022)
- [On the Adversarial Inversion of Deep Biometric Representations](https://mlsanity.com/paper/2304.05561/) (2023)

## Abstract (arXiv)

Deep learning-based models have been very successful in achieving state-of-the-art results in many of the computer vision, speech recognition, and natural language processing tasks in the last few years. These models seem a natural fit for handling the ever-increasing scale of biometric recognition problems, from cellphone authentication to airport security systems. Deep learning-based models have increasingly been leveraged to improve the accuracy of different biometric recognition systems in recent years. In this work, we provide a comprehensive survey of more than 120 promising works on biometric recognition (including face, fingerprint, iris, palmprint, ear, voice, signature, and gait recognition), which deploy deep learning models, and show their strengths and potentials in different applications. For each biometric, we first introduce the available datasets that are widely used in the literature and their characteristics. We will then talk about several promising deep learning works developed for that biometric, and show their performance on popular public benchmarks. We will also discuss some of the main challenges while using these models for biometric recognition, and possible future directions to which research in this area is headed.