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Related Experiment Video

Updated: Dec 25, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Presentation Attack Face Image Generation Based on a Deep Generative Adversarial Network.

Dat Tien Nguyen¹, Tuyen Danh Pham¹, Ganbayar Batchuluun¹

¹Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea.

Sensors (Basel, Switzerland)

|March 29, 2020

Summary

This summary is machine-generated.

This study introduces a novel method to generate fake face images for training presentation attack detection (PAD) systems. This approach enhances PAD system performance by overcoming the scarcity of real-world attack data.

Keywords:

artificial image generation generative adversarial network presentation attack detection presentation attack face images

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Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

Area of Science:

Biometrics
Computer Vision
Artificial Intelligence

Background:

Face-based biometric systems are widely used but vulnerable to presentation attacks (PAs).
Presentation Attack Detection (PAD) systems aim to identify fake biometric samples.
Current PAD systems suffer from limited performance due to insufficient training data of PAs.

Purpose of the Study:

To propose a method for artificially generating PA face images.
To address the challenge of limited PA data for training face-PAD systems.
To save time and resources in collecting PA samples.

Main Methods:

Utilizing a CycleGAN network, a deep learning framework, to learn characteristics of real and PA images.
Generating synthetic PA face images based on a few captured samples.
Proposing a new measurement method to evaluate the quality of generated PA images using a face-PAD system.

Main Results:

Generated PA face images effectively capture the characteristics of real PAs.
The synthetic images are suitable for training face-PAD systems.
Experiments on CASIA and Replay-mobile datasets validate the method's effectiveness.

Conclusions:

The proposed method offers a viable solution for augmenting PA datasets.
This approach can enhance the performance and robustness of face-PAD systems.
It represents a novel application of CycleGAN for generating PA face images in biometrics.