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 win- dowed fashion. Micro- and macro- facial expressions commonly exhibited by subjects are first magnified using Eulerian motion magnification. Next, two feature extraction algorithms, a con- figuration 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 pro- posed algorithm yields state-of-the-art performance and robust generalizability with low computational complexity.