Face Recognition Challenge: Object Recognition Approaches for Human/Avatar Classification
Recently, a novel "completely automated public Turing test to tell computers and humans apart (CAPTCHA)'' system has been proposed, in which users are asked to separate natural faces of humans and artificial faces of virtual world avatars. The system is based on the assumption that computers cannot separate them while it is an easy task for humans. Conventional digital forensics approaches to distinguish natural images from computer graphics images are mostly based on statistical analysis of the images such as noise in CMOS image sensors or Bayer matrix estimation. On the other hand, this paper uses face recognition and object classification based approaches. The experiments show that our approaches work surprisingly well and yields more than 99% accuracy. Our object classification based approach can also tell us how likely the input images are regarded as human/avatar faces.