Improving face authentication using virtual samples

We present a simple yet effective way of improving a face verification system by generating multiple virtual samples from the unique image corresponding to an access request. These images are generated using simple geometric transformations. This method is often used during training to improve the accuracy of a neural network model by making it robust against minor translation, scale and orientation changes. Our main contribution is to introduce such a method during testing. By generating N images from one single image and propagating them to a trained network model, one obtains N scores. By merging these scores using a simple mean operator, we show that the variance of merged scores is decreased by a factor between 1 and N. An experiment is carried out on the XM2VTS database which achieves new state-of-the-art performances.