Face Anti-Spoofing using Speeded-Up Robust Features and Fisher Vector Encoding

Zinelabidine Boulkenafet, Jukka Komulainen, Abdenour Hadid
2016 IEEE Signal Processing Letters  
The vulnerabilities of face biometric authentication systems to spoofing attacks have received a significant attention during the recent years. Some of the proposed countermeasures have achieved impressive results when evaluated on intra-tests i.e. the system is trained and tested on the same database. Unfortunately, most of these techniques fail to generalize well to unseen attacks e.g. when the system is trained on one database and then evaluated on another database. This is a major concern
more » ... biometric anti-spoofing research which is mostly overlooked. In this paper, we propose a novel solution based on describing the facial appearance by applying Fisher Vector encoding on Speeded-Up Robust Features (SURF) extracted from from different color spaces. The evaluation of our countermeasure on three challenging benchmark face spoofing databases, namely the CASIA Face Anti-Spoofing Database, the Replay-Attack Database and MSU Mobile Face Spoof Database, showed excellent and stable performance across all the three datasets. Most importantly, in inter-database tests, our proposed approach outperforms the state of the art and yields in very promising generalization capabilities, even when only limited training data is used.
doi:10.1109/lsp.2016.2630740 fatcat:htgqq3iv4zbiphezcx4rufnody