TY - GEN
T1 - Remote Photoplethysmography Correspondence Feature for 3D Mask Face Presentation Attack Detection
AU - Liu, Si Qi
AU - LAN, Xiangyuan
AU - YUEN, Pong Chi
N1 - Funding Information:
Acknowledgement. This project is partially supported by Hong Kong RGC General Research Fund HKBU 12201215.
PY - 2018
Y1 - 2018
N2 - 3D mask face presentation attack, as a new challenge in face recognition, has been attracting increasing attention. Recently, remote Photoplethysmography (rPPG) is employed as an intrinsic liveness cue which is independent of the mask appearance. Although existing rPPG-based methods achieve promising results on both intra and cross dataset scenarios, they may not be robust enough when rPPG signals are contaminated by noise. In this paper, we propose a new liveness feature, called rPPG correspondence feature (CFrPPG) to precisely identify the heartbeat vestige from the observed noisy rPPG signals. To further overcome the global interferences, we propose a novel learning strategy which incorporates the global noise within the CFrPPG feature. Extensive experiments indicate that the proposed feature not only outperforms the state-of-the-art rPPG based methods on 3D mask attacks but also be able to handle the practical scenarios with dim light and camera motion.
AB - 3D mask face presentation attack, as a new challenge in face recognition, has been attracting increasing attention. Recently, remote Photoplethysmography (rPPG) is employed as an intrinsic liveness cue which is independent of the mask appearance. Although existing rPPG-based methods achieve promising results on both intra and cross dataset scenarios, they may not be robust enough when rPPG signals are contaminated by noise. In this paper, we propose a new liveness feature, called rPPG correspondence feature (CFrPPG) to precisely identify the heartbeat vestige from the observed noisy rPPG signals. To further overcome the global interferences, we propose a novel learning strategy which incorporates the global noise within the CFrPPG feature. Extensive experiments indicate that the proposed feature not only outperforms the state-of-the-art rPPG based methods on 3D mask attacks but also be able to handle the practical scenarios with dim light and camera motion.
KW - 3D mask attack
KW - Face presentation attack detection
KW - Remote photoplethysmography
UR - http://www.scopus.com/inward/record.url?scp=85055111240&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01270-0_34
DO - 10.1007/978-3-030-01270-0_34
M3 - Conference proceeding
AN - SCOPUS:85055111240
SN - 9783030012694
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 577
EP - 594
BT - Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
A2 - Weiss, Yair
A2 - Ferrari, Vittorio
A2 - Sminchisescu, Cristian
A2 - Hebert, Martial
PB - Springer Verlag
T2 - 15th European Conference on Computer Vision, ECCV 2018
Y2 - 8 September 2018 through 14 September 2018
ER -