AcFR: Active Face Recognition Using Convolutional Neural Networks

Abstract

We propose AcFR, an active face recognition system that employs a convolutional neural network and acts consistently with human behaviors in common face recognition scenarios. AcFR comprises two main components—a recognition module and a controller module. The recognition module uses a pre-trained VGG-Face net to extract facial image features along with a nearest neighbor identity recognition algorithm. Based on the results, the controller module can make three different decisions—greet a recognized individual, disregard an unknown individual, or acquire a different viewpoint from which to reassess the subject, all of which are natural reactions when people observe passers-by. Evaluated on the PIE dataset, our recognition module yields higher accuracy on images under closer angles to those saved in memory. The accuracy is viewdependent and it also provides evidence for the proper design of the controller module.