Earticle

초록

영어

This paper presents a margin-based face liveness detection method with behavioral confirmation to prevent spoofing attacks using deep learning techniques. The proposed method provides a possibility to prevent biometric person authentication systems from replay and printed spoofing attacks. For this work, a set of real face images and fake face images was collected and a face liveness detection model is trained on the constructed dataset. Traditional face liveness detection methods exploit the face image covering only the face regions of the human head image. However, outside of this region of interest (ROI) might include useful features such as phone edges and fingers. The proposed face liveness detection method was experimentally tested on the author’s own dataset. Collected databases are trained and experimental results show that the trained model distinguishes real face images and fake images correctly.

목차

Abstract
1. INTRODUCTION
2. METHODOLOGIES
3. EXPERIMENTS
3.1 Dataset Preparation
3.2 Experimental Setup
3.3 Evaluation Metrics
4. RESULTS AND DISCUSSION
5. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Gabit Tolendiyev [ Doctoral Student, Department of Computer Engineering, Dongseo University, Korea ]
• Hyotaek Lim [ Professor, Department of Computer Engineering, Dongseo University, Korea ]
• Byung-Gook Lee [ Professor, Department of Computer Engineering, Dongseo University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

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• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380