Time-Aware Face Anti-Spoofing with Rotation Invariant Local Binary Patterns and Deep Learning

TL;DR

This paper proposes a novel face anti-spoofing method that combines rotation invariant local binary patterns with deep learning, incorporating time-aware strategies to improve detection accuracy against imitation attacks.

Contribution

It introduces a new approach that integrates rotation invariant features with time-aware deep learning for enhanced face anti-spoofing performance.

Findings

Achieves high classification accuracy in detecting spoofing attacks.

Demonstrates improved robustness over existing methods.

Validates effectiveness on benchmark datasets.

Abstract

Facial recognition systems have become an integral part of the modern world. These methods accomplish the task of human identification in an automatic, fast, and non-interfering way. Past research has uncovered high vulnerability to simple imitation attacks that could lead to erroneous identification and subsequent authentication of attackers. Similar to face recognition, imitation attacks can also be detected with Machine Learning. Attack detection systems use a variety of facial features and advanced machine learning models for uncovering the presence of attacks. In this work, we assess existing work on liveness detection and propose a novel approach that promises high classification accuracy by combining previously unused features with time-aware deep learning strategies.