Abstract:
For robust face biometrics, a reliable anti-spoofing
approach has become an essential pre-requisite against attacks.
While spoofing attacks are possible with any biometric modality,
face spoofing attacks are relatively easy which makes facial
biometrics especially vulnerable. This paper presents a new
framework for face spoofing detection in videos using motion
magnification and multifeature evidence aggregation in a windowed
fashion. Micro- and macro- facial expressions commonly
exhibited by subjects are first magnified using Eulerian motion
magnification. Next, two feature extraction algorithms, a configuration
of local binary pattern and motion estimation using
histogram of oriented optical flow, are used to encode texture and
motion (liveness) properties respectively. Multifeature windowed
videolet aggregation of these two orthogonal features, coupled
with support vector machine classification provides robustness
to different attacks. The proposed approach is evaluated and
compared with existing algorithms on publicly available Print
Attack, Replay Attack, and CASIA-FASD databases. The proposed
algorithm yields state-of-the-art performance and robust
generalizability with low computational complexity.
