Detecting planar surface using a light-field camera with application to distinguishing real scenes from printed photos

Abstract

We propose a novel approach for detecting printed photos from natural scenes using a light-field camera. Our approach exploits the extra information captured by a light-field camera and the multiple views of scene in order to infer a compact feature vector from the variance in the distribution of the depth of the scene. We then use this feature for robust detection of printed photos.Our algorithm can be used in person-based authentication applications to avoid intruding the system using a facial photo. Our experiments show that the energy of the gradients of points in the epipolar domain is highly discriminative and can be used to distinguish printed photos from original scenes.

Epipolar Plane Image Rectification and Flat Surface Detection in Light Field

“…Our detection precision is clearly superior to Ghasemi and Vetterli's method [14]. This improvement is prominent especially in the detection of natural scenes, where 7 natural scenes are misclassified as flat surface in [14] and only 3 in our method.…”

“…We implement the algorithm in the Matlab 2014b, on OS X 10.11.1 with 8 gigabytes of RAM and 2.7 GHz of processor. The running time of our implementation for a 9 × 9 × 768 × 768 × 3 light field is measured in seconds but does not excel the time complexity of [14]. This time can be accelerated to microseconds by using GPU.…”

“…The experimental environment and printed light fields can be seen in Figure 7. Apart from this, the EPFL light field dataset [14] is selected to do a comparison with the previous work. As this dataset is captured by a Lytro camera, the experimental results on this dataset can better reflect the pros and cons of the algorithm.…”

“…In order to better evaluate the accuracy of our algorithm in real data captured by light field camera, we run our code in another public light field dataset captured by Ghasemi and Vetterli [14]. The dataset consists of 50 light fields of printed photos and 50 light fields of natural scenes.…”

“…Undoubtedly, the method also suffers the same problem, that is, inaccuracy of disparity estimation. Ghasemi and Vetterli [14] proposed to extract energy feature vector based on the change of gradient of EPI and to distinguish the flat surface from nonflat one by using a Bayes classifier. The method computes the slope of all epipolar lines and then takes the variance; it is still a depthbased method.…”