Liveness Detection in Computer Vision: Transformer-based Self-Supervised Learning for Face Anti-Spoofing

TL;DR

This paper demonstrates that a Vision Transformer model, fine-tuned with self-supervised learning, outperforms traditional CNNs in face anti-spoofing tasks, enhancing biometric security against spoofing attacks.

Contribution

It introduces a transformer-based approach using DINO for self-supervised learning, showing improved accuracy and robustness over CNN models in face anti-spoofing.

Findings

ViT with DINO outperforms CNN in accuracy

Transformer models better detect complex spoofing cues

Model validated on standard and new datasets

Abstract

Face recognition systems are increasingly used in biometric security for convenience and effectiveness. However, they remain vulnerable to spoofing attacks, where attackers use photos, videos, or masks to impersonate legitimate users. This research addresses these vulnerabilities by exploring the Vision Transformer (ViT) architecture, fine-tuned with the DINO framework. The DINO framework facilitates self-supervised learning, enabling the model to learn distinguishing features from unlabeled data. We compared the performance of the proposed fine-tuned ViT model using the DINO framework against a traditional CNN model, EfficientNet b2, on the face anti-spoofing task. Numerous tests on standard datasets show that the ViT model performs better than the CNN model in terms of accuracy and resistance to different spoofing methods. Additionally, we collected our own dataset from a biometric application to validate our findings further. This study highlights the superior performance of transformer-based architecture in identifying complex spoofing cues, leading to significant advancements in biometric security.