TY - GEN
T1 - Face liveliness detection based on texture and color features
AU - Song, Li
AU - Ma, Hongbin
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - Nowadays, face recognition has been used in many security occasions, but few of them have ability to distinguish real and fake faces. Besides, many researches on face liveliness detection mainly focused on intrusive methods, which are not user-friendly in practice. This paper proposes a novel non-intrusive face liveliness detection method based on the analysis of texture and color features. More specifically, this method adopts an improved local ternary pattern (LTP) to classify the nearby pixels. Based on the face pixel analysis, the infinity norm of pixel matrices is added as new features. The effectiveness of feature selection has been validated by different kinds of experiments on three challenging face anti-spoofing databases (NUAA, CASIA FASD and Replay-attack). This method reaches a compromise between number of features and accuracy, which means it also works on embedded systems.
AB - Nowadays, face recognition has been used in many security occasions, but few of them have ability to distinguish real and fake faces. Besides, many researches on face liveliness detection mainly focused on intrusive methods, which are not user-friendly in practice. This paper proposes a novel non-intrusive face liveliness detection method based on the analysis of texture and color features. More specifically, this method adopts an improved local ternary pattern (LTP) to classify the nearby pixels. Based on the face pixel analysis, the infinity norm of pixel matrices is added as new features. The effectiveness of feature selection has been validated by different kinds of experiments on three challenging face anti-spoofing databases (NUAA, CASIA FASD and Replay-attack). This method reaches a compromise between number of features and accuracy, which means it also works on embedded systems.
KW - LBP
KW - LTP
KW - color moment
KW - infinity norm
KW - liveliness detection
UR - http://www.scopus.com/inward/record.url?scp=85067429908&partnerID=8YFLogxK
U2 - 10.1109/ICCCBDA.2019.8725639
DO - 10.1109/ICCCBDA.2019.8725639
M3 - Conference contribution
AN - SCOPUS:85067429908
T3 - 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019
SP - 418
EP - 422
BT - 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2019
Y2 - 12 April 2019 through 15 April 2019
ER -