Eyeblink-based Anti-Spoofing in Face Recognition from a Generic Webcamera

TLDR

Eyeblink sequences often have a complex underlying structure. We present a real‑time liveness detection approach against photograph spoofing in face recognition by recognizing spontaneous eyeblinks in a non‑intrusive manner. The method uses a generic webcam and models blink detection as inference in an undirected conditional graphical framework, learning compact observation and transition potentials and incorporating an eye‑closeness measure derived from adaptive boosting. Extensive experiments demonstrate that the approach outperforms cascaded AdaBoost and HMM for eyeblink detection.

Abstract

We present a real-time liveness detection approach against photograph spoofing in face recognition, by recognizing spontaneous eyeblinks, which is a non-intrusive manner. The approach requires no extra hardware except for a generic webcamera. Eyeblink sequences often have a complex underlying structure. We formulate blink detection as inference in an undirected conditional graphical framework, and are able to learn a compact and efficient observation and transition potentials from data. For purpose of quick and accurate recognition of the blink behavior, eye closity, an easily-computed discriminative measure derived from the adaptive boosting algorithm, is developed, and then smoothly embedded into the conditional model. An extensive set of experiments are presented to show effectiveness of our approach and how it outperforms the cascaded Adaboost and HMM in task of eyeblink detection.