Azeddine Benlamoudi, Kamal Eddine Aiadi, Abdelkrim Ouafi, Djamel Samai, and Mourad Oussalah "Face antispoofing based on frame difference and multilevel representation," Journal of Electronic Imaging 26(4), 043007 (21 July 2017). https://doi.org/10.1117/1.JEI.26.4.043007
Face antispoofing based on frame difference and multilevel representation
|Author:||Benlamoudi, Azeddine1; Aiadi, Kamal Eddine1; Ouafi, Abdelkrim2;|
1University of Ouargla, Faculté des Nouvelles Technologies de l’information et de la communication, Laboratoire de Génie Électrique (LAGE), Ouargla, Algeria
2University of Biskra, Laboratory of LESIA, Algeria
3University of Oulu, Center for Ubiquitous Computing, Finland
|Online Access:||PDF Full Text (PDF, 3.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019061720649
|Publish Date:|| 2019-06-17
Due to advances in technology, today’s biometric systems become vulnerable to spoof attacks made by fake faces. These attacks occur when an intruder attempts to fool an established face-based recognition system by presenting a fake face (e.g., print photo or replay attacks) in front of the camera instead of the intruder’s genuine face. For this purpose, face antispoofing has become a hot topic in face analysis literature, where several applications with antispoofing task have emerged recently. We propose a solution for distinguishing between real faces and fake ones. Our approach is based on extracting features from the difference between successive frames instead of individual frames. We also used a multilevel representation that divides the frame difference into multiple multiblocks. Different texture descriptors (local binary patterns, local phase quantization, and binarized statistical image features) have then been applied to each block. After the feature extraction step, a Fisher score is applied to sort the features in ascending order according to the associated weights. Finally, a support vector machine is used to differentiate between real and fake faces. We tested our approach on three publicly available databases: CASIA Face Antispoofing database, Replay-Attack database, and MSU Mobile Face Spoofing database. The proposed approach outperforms the other state-of-the-art methods in different media and quality metrics.
Journal of electronic imaging
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
© 2017 SPIE and IS&T.