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ECCV
2010
Springer

Face Liveness Detection from A Single Image with Sparse Low Rank Bilinear Discriminative Model

9 years 5 months ago
Face Liveness Detection from A Single Image with Sparse Low Rank Bilinear Discriminative Model
Spoofing with photograph or video is one of the most common manner to circumvent a face recognition system. In this paper, we present a real-time and non-intrusive method to address this based on individual images from a generic webcamera. The task is formulated as a binary classification problem, in which, however, the distribution of positive and negative are largely overlapping in the input space, and a suitable representation space is hence of importance. Using the Lambertian model, we propose two strategies to extract the essential information about different surface properties of a live human face or a photograph, in terms of latent samples. Based on these, we develop two new extensions to the sparse logistic regression model which allow quick and accurate spoof detection. Primary experiments on a large photo imposter database show that the proposed method gives preferable detection performance compared to others.
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2010
Where ECCV
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