Face liveness detection from a single image with sparse low rank bilinear discriminative model

Authors:
Xiaoyang Tan;Yi Li;Jun Liu;Lin Jiang
Affiliations:
Dept. of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China;Dept. of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China;Dept. of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China;Dept. of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China
Venue:
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Year:
2010

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Abstract

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.