Generation of facial image samples for boosting the performance of face recognition systems

Abstract

We tackle the problem of insufficient training samples which often leads to degraded performance for face recognition systems. First, we propose an efficient method for matching two facial images that does not require 3D information. We then apply the proposed face matching algorithm to morph a source image into a target image, thereby generating a large number of facial images with expressions or lighting conditions in-between that of the source and target images. These generated images are used to greatly expand the set of training samples in a face recognition system. Experiments show that by incorporating these large number of generated facial images in the training process, the recognition rate for test samples is boosted up by a large margin.