Face Liveness Detection Using Synthetic Wrap Attack Images
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Solution Overview
Problem
Biometric systems are vulnerable to wrap attacks, which involve inexpensive printed masks that deceive depth-based detection techniques, and current liveness detection methods are costly and complex, lacking effective training datasets for such attacks.
Innovation Solution
A method using a variational autoencoder-generative adversarial network (VAE-GAN) generates synthetic wrap attack images from bona fide samples to create a training dataset for a liveness detection model, incorporating a support vector machine (SVM) for accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If depth-based liveness detection mechanisms are used, then detection capability is improved, but device complexity and cost increase due to additional sensors
Solution Approach 1:
The patent creates synthetic wrap attack images by copying and transforming bona fide face images through neural networks. These synthetic attack images replicate the characteristics of real wrap attacks without requiring actual physical masks or complex sensor setups, enabling detection training using only standard cameras
Solution Approach 2:
The patent replaces expensive, complex depth sensors and thermal cameras with inexpensive standard cameras. By generating synthetic attack data through computational methods rather than capturing real attacks with specialized hardware, the system achieves detection capability at minimal cost
2Reliability
If thermal imaging-based liveness detection is used, then detection capability is improved, but cost and complexity increase
Solution Approach 1:
The patent generates synthetic wrap attack images by copying bona fide face images through neural network transformations. This approach creates realistic attack simulations without requiring expensive thermal imaging hardware, maintaining detection capability while dramatically reducing system cost
Solution Approach 2:
The patent replaces physical thermal imaging sensors with computational neural networks. Instead of using expensive thermal hardware to detect attacks, the system uses software-based synthetic image generation to create training data, substituting mechanical/physical detection mechanisms with computational methods
3Manufacturing precision
If synthetic images are generated using neural networks, then training data quality is improved, but computational resources and time increase
Solution Approach 1:
The patent pre-generates synthetic wrap attack images using neural networks during the training phase. By creating these synthetic training data in advance before actual detection is needed, the system prepares high-quality training material that enables fast and accurate real-time detection without requiring time-consuming computational processes during operation
Data Source
AI summary
Provided are a method, device, and system for generating a liveness detection dataset, training a liveness detection model, and performing liveness detection. The method includes: obtaining a plurality of real images of faces; providing the plurality of real images to a neural network; generating a plurality of synthetic images corresponding to the plurality of real images based on an output of the neural network; and training a liveness detection model based on the plurality of real images and the plurality of synthetic images, wherein the liveness detection model is used to perform liveness detection by determining whether an input image of a face includes a live image of the face.