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

Abstract

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 in 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.

Face De-spoofing: Anti-spoofing via Noise Modeling

“…Texture-based Face Anti-spoofing Texture analysis is widely adopted in face antispoofing as well as other computer vision tasks [19,20], where defining an effective feature representation is the key endeavor. Early works apply the hand-crafted feature descriptors, such as LBP [3,4,21], HoG [5,6], SIFT [22] and SURF [23], to project the faces to a low-dimension embedding. However, those hand-crafted features are not specifically designed to capture the subtle differences in the spoofing faces, and thus the embedding may not be discriminative.…”

Deep Tree Learning for Zero-Shot Face Anti-Spoofing

“…Later, research move to a more general texture analysis and address print and replay attacks. Researchers mainly utilize handcrafted features, e.g., LBP [7,16,17,35], HoG [25,47], SIFT [38] and SURF [8], with traditional classifiers, e.g., SVM and LDA, to make a binary decision. Those methods perform well on the testing data from the same database.…”

FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-Spoofing

“…1) Crafted features detection: Various filters were used to detect the points to present the feature. The widely adopted features include: Local Binary Patterns (LBP) [10, 3, 1], Scale Invariant Feature Transform (SIFT) [11], Speeded-Up Robust Features (SURF) [4], histogram of oriented gradients (HOG) [2,12], Difference of Gaussian (DoG) [12].…”