# Deep Learning-Based Multi-Factor Authentication: A Survey of Biometric and Smart Card Integration Approaches

arXiv: https://arxiv.org/abs/2510.05163 · HTML: https://mlsanity.com/paper/2510.05163/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 4 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [Learning to Authenticate with Deep Multibiometric Hashing and Neural Network Decoding](https://mlsanity.com/paper/1902.04149/) (2019)
- [Data-driven behavioural biometrics for continuous and adaptive user verification using Smartphone and Smartwatch](https://mlsanity.com/paper/2110.03149/) (2021)
- [Multi-modal biometric authentication: Leveraging shared layer architectures for enhanced security](https://mlsanity.com/paper/2411.02112/) (2024)

## Built on by (most cited first, 2 of 2)

- [ISO/IEC-Compliant Match-on-Card Face Verification with Short Binary Templates](https://mlsanity.com/paper/2510.16078/) (2025)
- [Ageing Drift in Binary Face Templates: A Bits-per-Decade Analysis](https://mlsanity.com/paper/2510.21778/) (2025)

## Abstract (arXiv)

In the era of pervasive cyber threats and exponential growth in digital services, the inadequacy of single-factor authentication has become increasingly evident. Multi-Factor Authentication (MFA), which combines knowledge-based factors (passwords, PINs), possession-based factors (smart cards, tokens), and inherence-based factors (biometric traits), has emerged as a robust defense mechanism. Recent breakthroughs in deep learning have transformed the capabilities of biometric systems, enabling higher accuracy, resilience to spoofing, and seamless integration with hardware-based solutions. At the same time, smart card technologies have evolved to include on-chip biometric verification, cryptographic processing, and secure storage, thereby enabling compact and secure multi-factor devices. This survey presents a comprehensive synthesis of recent work (2019-2025) at the intersection of deep learning, biometrics, and smart card technologies for MFA. We analyze biometric modalities (face, fingerprint, iris, voice), review hardware-based approaches (smart cards, NFC, TPMs, secure enclaves), and highlight integration strategies for real-world applications such as digital banking, healthcare IoT, and critical infrastructure. Furthermore, we discuss the major challenges that remain open, including usability-security tradeoffs, adversarial attacks on deep learning models, privacy concerns surrounding biometric data, and the need for standardization in MFA deployment. By consolidating current advancements, limitations, and research opportunities, this survey provides a roadmap for designing secure, scalable, and user-friendly authentication frameworks.