A Novel Active Solution for Two-Dimensional Face Presentation Attack Detection

TL;DR

This paper introduces an efficient, training-free, and robust active method for detecting 2D face presentation attacks, addressing limitations of existing data-dependent approaches in biometric security.

Contribution

The paper proposes a novel active presentation attack detection technique that does not require training data and is effective against 2D attacks, improving robustness and user-friendliness.

Findings

Method is training-free and data-independent

Effective on low-quality images and diverse user ages

Highly robust to 2D presentation attacks

Abstract

Identity authentication is the process of verifying one's identity. There are several identity authentication methods, among which biometric authentication is of utmost importance. Facial recognition is a sort of biometric authentication with various applications, such as unlocking mobile phones and accessing bank accounts. However, presentation attacks pose the greatest threat to facial recognition. A presentation attack is an attempt to present a non-live face, such as a photo, video, mask, and makeup, to the camera. Presentation attack detection is a countermeasure that attempts to identify between a genuine user and a presentation attack. Several industries, such as financial services, healthcare, and education, use biometric authentication services on various devices. This illustrates the significance of presentation attack detection as the verification step. In this paper, we study state-of-the-art to cover the challenges and solutions related to presentation attack detection in a single place. We identify and classify different presentation attack types and identify the state-of-the-art methods that could be used to detect each of them. We compare the state-of-the-art literature regarding attack types, evaluation metrics, accuracy, and datasets and discuss research and industry challenges of presentation attack detection. Most presentation attack detection approaches rely on extensive data training and quality, making them difficult to implement. We introduce an efficient active presentation attack detection approach that overcomes weaknesses in the existing literature. The proposed approach does not require training data, is CPU-light, can process low-quality images, has been tested with users of various ages and is shown to be user-friendly and highly robust to 2-dimensional presentation attacks.