This document summarizes a research paper on face anti-spoofing using deep learning models. It discusses using auxiliary supervision from additional data sources like depth maps and remote photoplethysmography (rPPG) signals to improve spoof detection performance. The proposed method uses a CNN to extract image features and an RNN to model rPPG signals. It evaluates the approach on the Spoof in the Wild database containing live and spoof videos, and compares error rates to other databases. The document provides background on anti-spoofing, defines relevant terms like rPPG and error metrics, and references related works and datasets.

1. Susang Kim(healess1@gmail.com)
Anti-Spoofing
Learning Deep Models for Face Anti-Spoofing:
Binary or Auxiliary Supervision

2. Anti-Spoofing 관련 아래 논문을 바탕으로 설명
[논문]
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision
In Proceeding of IEEE Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, Jun. 2018
http://cvlab.cse.msu.edu/pdfs/Liu_Jourabloo_Liu_CVPR2018.pdf
[Data]
Spoof in the Wild (SiW) Face Anti-spoofing Database
http://cvlab.cse.msu.edu/spoof-in-the-wild-siw-face-anti-spoofing-database.html

3. 얼굴인식 실제 서비스로 이어지기 위해서는...
실재 얼굴과 Print,Phone(Galaxy,iPhone),
가면, 동영상등을 구분해야함
[출처] : http://biometrics.cse.msu.edu/projects/face_recog_spoofing.html

4. By Auxiliary Supervision
RGB이미지를 바탕으로 Depth정보를 생성하고 rPPG정보로 분류

5. What is rPPG?
[출처] : https://www.noldus.com/facereader/remote-photoplethysmography-facereader

6. Typical Application of rPPG
Desktop implementation of Remote Photoplethysmography – Measuring heart rate using facial video.
(https://github.com/prouast/heartbeat)

7. Proposed Method
CNN으로 이미지 정보를 학습 rPPG정보를 RNN으로 학습하여 구성

8. Network Architecture (CNN+RNN)

9. Depthmap (3D Reconstruction)

10. rPPG Supervision

11. Non-rigid Registration
Feature Map과 DepthMap 그리고 3D Shape의 3가지 Input
=> frontalized F output

12. Spoof in the Wild Database
data from live and spoof viedos

13. Anti-Spoof Data
Datasets # of subj. / # of
sess.
Links Year Spoof attacks
attacks
Publish
Time
NUAA 15/3 Download 2010 Print 2010
CASIA-MFSD 50/3 Download(link failed) 2012 Print, Replay 2012
Replay-Attack 50/1 Download 2012 Print, 2 Replay 2012
MSU-MFSD 35/1 Download 2015 Print, 2 Replay 2015
MSU-USSA 1140/1 Download 2016 2 Print, 6 Replay 2016
Oulu-NPU 55/3 Download 2017 2 Print, 6 Replay 2017
Siw 165/4 Download 2018 2 Print, 4 Replay 2018

14. Experimental Comparison
Attack Presentation Classification Error Rate APCER
Bona Fide Presentation Classification Error Rate BPCER ACER =
(APCER+BPCER) / 2
Half Total Error Rate HTER
The HTER is half of the summation of the False Rejection Rate
(FRR) and the False Acceptance Rate (FAR)
Oulu protocols : OULU-NPU
a mobile face presentation attack database with real-world variations
[출처] : TDR/ FDR http://www.mohr-engineering.com/TDR_vs_FDR_Distance_to_Fault-A.php

15. Example

16. Thanks
Any Questions?
You can send mail to
Susang Kim(healess1@gmail.com)