TY - GEN
T1 - FACE LIVENESS DETECTION AND RECOGNITION USING SHEARLET BASED FEATURE DESCRIPTORS
AU - Li, Yuming
AU - Po, Lai-Man
AU - Xu, Xuyuan
AU - Feng, Litong
AU - Yuan, Fang
PY - 2016/3
Y1 - 2016/3
N2 - Face recognition is a widely used biometric technology due to its convenience but it is vulnerable to spoofing attacks made by non-real faces such as a photograph or video of valid user. Face liveness detection is a core technology to make sure that the input face is a live person. However, this is still very challenging using conventional liveness detection approaches of texture analysis and motion detection. The aim of this paper is to develop a multifunctional feature descriptor and an efficient framework which can be used to deal with both face liveness detection and recognition. In this framework, new feature descriptors are defined using a multiscale directional transform (shearlet transform). Then, stacked autoencoders and softmax classifier are concatenated to detect face liveness and identify person. We evaluated this approach using CASIA Face Anti-Spoofing Database and the results show that our approach performs better than state-of-the-art techniques following the provided evaluation protocols of this database, and is possible to significantly enhance the security of face recognition biometric system.
AB - Face recognition is a widely used biometric technology due to its convenience but it is vulnerable to spoofing attacks made by non-real faces such as a photograph or video of valid user. Face liveness detection is a core technology to make sure that the input face is a live person. However, this is still very challenging using conventional liveness detection approaches of texture analysis and motion detection. The aim of this paper is to develop a multifunctional feature descriptor and an efficient framework which can be used to deal with both face liveness detection and recognition. In this framework, new feature descriptors are defined using a multiscale directional transform (shearlet transform). Then, stacked autoencoders and softmax classifier are concatenated to detect face liveness and identify person. We evaluated this approach using CASIA Face Anti-Spoofing Database and the results show that our approach performs better than state-of-the-art techniques following the provided evaluation protocols of this database, and is possible to significantly enhance the security of face recognition biometric system.
KW - Anti-spoofing
KW - Face recognition
KW - Liveness detection
KW - Shearlet transform
KW - Softmax classification
KW - Stacked autoencoders
UR - http://www.scopus.com/inward/record.url?scp=84973351661&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84973351661&origin=recordpage
U2 - 10.1109/ICASSP.2016.7471800
DO - 10.1109/ICASSP.2016.7471800
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781479999873
SP - 874
EP - 877
BT - 2016 IEEE International Conference on Acoustics, Speech, and Signal Processing - PROCEEDINGS
PB - IEEE
T2 - 41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016)
Y2 - 20 March 2016 through 25 March 2016
ER -