Real-time face swapping as a tool for understanding infant   self-recognition
  To study the preference of infants for contingency of movements and familiarity of faces during self-recognition task, we built, as an accurate and instantaneous imitator, a real-time face- swapper for videos. We present a non-constraint face-swapper based on 3D visual tracking that achieves real-time performance through parallel computing. Our imitator system is par- ticularly suited for experiments involving children with Autistic Spectrum Disorder who are often strongly disturbed by the constraints of other methods. 
Sao Mai Nguyen (INRIA Bordeaux - Sud-Ouest), Masaki Ogino, Minoru   Asada
cs.AI cs.CV